Cognitive Mapping and GIS for Community-Based Resource Identification Figure Education resources: Cognitive mapping vs resource guides Figure Health care resources: Cognitive mapping vs resource guides 380 Cognitive Mapping and GIS for Community-Based Resource Identification County resource guides failed to provide adequate information Except in education and childcare, the resource guides fell far short of the number of resources identified by the mapping participants While there are many childcare resources in the Jefferson County information guides, there is little overlap between the childcare listed in the Jefferson County guides and the childcare identified by the cognitive mapping The GIS maps effectively demonstrate such knowledge gaps Second, there are a significant number of resources in Denver County (east of Jefferson County) that providers and clients identify Reasonable accessibility to Denver County, as well as lack of availability of the resources in Jefferson County, likely accounts for this trend Building a community-based SOC will require Jefferson County to find ways to offer some of these services locally, a challenge that will require developing community partnerships to overcome the financial constraints which the County faces Third, opposite of the previous trend, Jefferson County resource guides provide mainly Denver locations for some types of resources, even though the same resources exist in numerous places in Jefferson County Available resources closer to Jefferson County residents are a fundamental component of SOC and, in this trend, require only disseminating the information effectively, which is a low-cost method to improve community-based service delivery Finally, there is a large disparity in knowledge between clients and providers With the exception of of the 24 categories, Education, Recreation, and Commercial Resources, the providers and clients did not overlap significantly in knowledge about resources Providers know more about traditional resources such as other agencies or governmentally-supported social services, while clients know about resources of a less traditional nature, such as churches, motels, and parks where teenagers gathered to socialize and engage in recreational sports activities Although these informal resources are not referral services that providers typically pass along to clients, they are important community-based resources to share with clients In creating a community-based SOC, providers need to be aware of the alternative methods clients use to meet their needs In some instances, this new information will lead to the creation of government/community partnerships to more effectively and efficiently deliver services In other circumstances, the additional knowledge of resources will provide clients with options and/ or fill gaps in needs that traditional government and community providers cannot meet Lessons Le arned Several problems directly and indirectly related to the GIS component of the project became apparent and required adjustments to the procedures or accommodations to the expected output These include research procedures that are incompatible with social service agencies’ capacity, issues of client confidentiality, repeat rates, incomplete and/or inaccurate databases for coding resource locations, coding protocols, and mapping accuracy First, as has been found before, many county and local agencies lack leadership that understands the value of GIS in policy decision-making (Greene, 2000; Nedovic-Budic, 1996; Ventura, 1995; Worrall & Bond, 1997) Hence, many agencies lack the technical ability to employ GIS and, consequently, also lack the understanding to work effectively and efficiently with the researchers Furthermore, because social service agencies typically not have a GIS analyst on staff, data and map files have limited usefulness beyond the initial analysis as presented in the final report Finally, human service agencies have organizational procedures that create significant barriers in implementing research projects, barriers that need to be addressed in the project planning stages (Ventura, 1995) Jefferson County Human Services suffered from all three impediments 381 Cognitive Mapping and GIS for Community-Based Resource Identification and was exacerbated by the high turnover of the staff In the first year, two-thirds of the project staff left By the middle of the second year, only one person out of nine key project staff remained Those who left included the project manager and the principal investigator, both of who had been replaced twice Within 18 months, none of the people who conceptualized and wrote the HHS grant were involved in the project Institutional memory was wiped clean and new staff was unfamiliar and wary of many components laid out in the grant proposal, including the untraditional resource identification method Higher administrative support for the innovative project waned, and “business as usual” reasserted itself as the dominant paradigm It became clear that the resource database developed through the mapping process would not be updated on a regular basis and, perhaps, not disseminated throughout the organization if left to Jefferson County The CIPP sought out a more stable organization to house the resource data, Colorado 2-1-1, with the permission of the first project manager Second, human service agencies as well as educational institutions cannot share client/student data This presents a significant research barrier when the project requires participation of these populations Ideally, individuals within the organizations would have both the access to the data and sophistication to manipulate the data in accordance with standard research protocols This is unlikely to be the case in institutions which are financially strapped and lack the vision or political will to invest in trained personnel and needed research tools To ameliorate these conditions, project planning must include agreed-upon protocols for effectively and efficiently handling confidential data Third, unique to this project was the creation of a “repeat rate” to set a standard for data density The 80% repeat rate was selected for efficiency of resources, based on an extrapolation of the average number of points per map and time needed to code and enter the data for 382 each map Unknown was how many participants/ maps were needed to reach the 80% repeat rate in each of the 24 categories Initially, the CIPP recommended target was 450 participants This number was revised downward by Jefferson County Human Services to a maximum of 250 participants From the 247 actual participants, the 80% repeat rate was reached in only two of the 24 resource categories The average repeat rate was 55% across all categories, indicating that more than 250 participants were needed to reach 80% Whether 450 participants were ultimately required is unknown More importantly, did the lower repeat rate significantly affect the quality of the project? Certainly, fewer resources were identified at the 55% rate; but 1,480 resources not in Jefferson County resource guides were identified; not an insignificant contribution to building a more comprehensive social services Fourth, in the process of coding the maps and sorting the data to find repeated addresses or groupings by type of provider, and so forth, it was discovered that precise alphanumeric coding was critical With the large number of data fields (attributes) assigned to each participant, there were inconsistencies in some of the categories The data cleaning was more extensive than anticipated Future projects should utilize numeric coding in attributes to the fullest extent possible and develop strict alphanumeric standards for addresses, organizational names, and other alpha fields Finally, to find resource addresses, MapQuest and the Denver metro area phone book were used MapQuest was the most efficient method but had the most errors, as discovered when the address was imported into ArcMap A cross-check with the phone books corrected most of these errors Nine percent of the mapping points were unidentifiable due to a combination of missing information in MapQuest and the phone book, and poor location information on the hand drawn maps The latter accounted for a greater proportion of the unidentified points, especially resources such as neighborhood parks and unnamed resources Cognitive Mapping and GIS for Community-Based Resource Identification such as “soup kitchen.” Rather than rely solely on participants naming the nearest cross streets to such resources, the closest known commercial entity should be identified This redundancy will reduce missing data due to participant error in naming streets F uture T rends While this project was limited to identifying resources, spatial patterns of resource locations, and knowledge gaps, the collected data can be mined further More specific uses can be created, such as a searchable Web-based provider resource database and the identification of physical and/or service areas with inadequate resources in relation to socio-economic deprivation areas The latter allows providers to demonstrate specific needs, important for several reasons, including the pursuit of future programmatic funding These specific uses are described in greater detail as follows: • Provider resource database: In the future, the Web-based database can be converted into a tool for social service providers to identify available resources and the most accessible locations for clients (Worrall & Bond, 1997) The end user (a case-worker) would be able to search for particular resources based on any number of criteria or a combination of criteria For example, one might enter necessary criteria such as Rental Assistance Housing Resource located within three miles of a given location that also caters to Spanish-speaking clientele After these attributes or criteria are entered into the appropriate locations on the Webpage, a list of all the resources or providers that fit the criteria could be retrieved, similar to the business name search feature available through a site such as MapQuest Finally, digital maps could be generated with driving directions for the case-worker to print out • for the client It is also possible to map the public transportation routes to services Needs assessments: The database can be used to conduct comprehensive, quantifiable, and defensible needs assessments A social service provider administrator or grant writer could search the data described above in conjunction with Census data and the County’s client locations to reveal areas of need or areas of excess (Bond & Devine, 1991; Worrall & Bond, 1997).6 A strategic plan could be developed to determine where a new office or access point for a particular resource should be located to serve the greatest number of clients This type of spatial analysis based on quantifiable numbers and distances can be used to justify a particular course of action either for internal/external accountability or to acquire funding for various projects aimed at community resource and social service distribution Acknow ledg ments The author would like to thank April Smith, Department of Psychology, Colorado State University, and Mary Tye, Department of Psychology, Colorado State University, for running the workshops and coding the data; David Wallick, Colorado Institute of Public Policy, Colorado State University, for conducting the GIS analysis; and Juliana Hissrich for providing administrative support to the project C onc lus ion Cognitive mapping combined with GIS analysis is a powerful method for identifying community resources by providing: (1) a comprehensive database of existing services; (2) a basis to build communication networks and cooperation among government and community providers; (3) the 383 Cognitive Mapping and GIS for Community-Based Resource Identification ability to create an efficient system that avoids duplication of efforts; (4) an understanding of the geographical distribution of resources; (5) the identification of resources lacking in the county and specific communities; and (6) knowledge differences among diverse participant groups The addition of 1,480 resource locations within the seven study areas (only a portion of Jefferson County) nearly tripled the number of resources and services listed in the Jefferson County guides Ultimately, service delivery in SOC is about building partnerships across the multiple services and bringing in new, even sometimes untraditional, community partners Family involvement is the key in this collaborative arrangement Similar to untraditional community partners and resources, families as partners not fit easily within current social service delivery structures, values, and beliefs Recognizing, valuing, and partnering with resource providers identified by clients and community members is one important step toward shifting practices Cognitive mapping with GIS 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use of geographical information systems in local government Public Administration Review, 55, 461-467 Worrall, L., & Bond, D (1997) Geographical information systems, spatial analysis, and public policy: The British experience International Statistical Review, 65, 365-379 Yoshino, R (1991) A note on cognitive maps: An optimal spatial knowledge representation Journal of Mathematical Psychology, 35, 371-393 385 Cognitive Mapping and GIS for Community-Based Resource Identification E ndnotes 386 The project was supported by grant #90CA1715/01, CFDA #93.570 from the Federal Department of Health and Human Services through Jefferson County, Colorado The term cognitive mapping is used for a variety of techniques, including “fuzzy cognitive mapping,” a technique that builds mental maps of perceptions from focus-group and interviews (Hjortso, Christensen, & Tarp, 2005; Hobbs et al., 2002) In this project, cognitive mapping means hand-drawn maps of tangible community resources and locations, a geographical data collection technique new to GIS Nine percent of the mapping points could not be accurately located and were dropped from the analysis Of the remaining 89%, two possible location errors could occur in transferring the cognitive map information into a database for ArcMap First, multiple coders could use different alphanumeric codes, thereby making the same resource appear as a different resource To correct this error, the data was cleaned by conducting sorts on multiple columns in the excel spreadsheet to reveal unknown duplicates For example, a search on “Research Name” might find the same resource with inconsistent address codes If the address did not match exactly (e.g., one was coded with “St.” and another coded with “Street,” the coding was corrected to be consistent Similar searches were done on other categories such as street address, street name, and zip code The data was cleaned accordingly The second error was from incorrect addresses in the MapQuest and/or Dex directory The Dex directory is the official metropolitan phone and address directory and should have a high level of reliability; however, the actual reliability rate is unknown To correct for possible errors, all identified social services not in the Jefferson County resource guides (e.g., soup kitchens, English as a Second Language courses, support groups, etc.) were called to verify the address It was assumed that the Jefferson County resource guides had accurate information All identified resources were provided to Colorado’s 2-1-1 system, which is the national abbreviated dialing code for free access to health and human services information and referral (I&R) 2-1-1 is an easyto-remember and universally-recognizable number that makes a critical connection between individuals and families in need and the appropriate community-based organizations and government agencies Housing the data with 2-1-1 allows statewide access to resources and bi-annual updating to keep the information current Colorado 2-1-1 system is the depository for the resources collected in this project Web searchable database of resources can be found at http://211colorado org/ CIPP provided Jefferson County with the ethnic enclave areas based on the 2000 Census The Asian communities fell outside the project boundaries set by Jefferson County (see Figure 1) and, unlike Russians, Latinos, and Native Americans, Jefferson County did not request mapping with the Asian community For example, it might be found that 65% of all users of a certain type of resource (this data would be collected by cognitive mapping alone) live “x” number of miles away (analysis performed by the GIS system) from a particular needed or frequently-accessed resource (gathered through cognitive mapping and other sources) Cognitive Mapping and GIS for Community-Based Resource Identification Append ix Only forty percent of the participants provided demographic information, which limits the ability to determine the gender, age, and ethnicity/race of the participants However, there is no way to determine the representativeness of the sample on these traditional demographics since the population characteristics are unknown Even among the clients, the demographics are not available because most of the client records were incomplete Unlike many social research projects, demographic representation is less of a concern For the identification of resources, a cross-section of the types of people who use or provide services and the geographical distribution of their knowledge was most important, of which both criteria were met Table Demographics of participants (n=100) Demographic characteristic All participants (n=100) Providers (n=19) Clients (n=72) Community Residents (n=9) Number and percent female 85% 90% 82% 100% Average age 34.39 39.75 31.86 44.83 Number and percent Caucasian 62% 68% 64% 33% Number and percent Latino 19% 5% 24% 11% Number and percent African American 6% 0% 4% 33% Number and percent Native American 9% 21% 4% 22% Number and percent Other 4% 5% 3% 0% This work was previously published in Emerging Spatial Information Systems and Applications, edited by B Hilton, pp 326350, copyright 2007 by IGI Publishing (an imprint of IGI Global) 387 388 Chapter XLV Collaborative Mapping and GIS: An Alternative Geographic Information Framework Edward Mac Gillavry Webmapper, The Netherlands Abstr act The collection and dissemination of geographic information has long been the prerogative of national mapping agencies Nowadays, location-aware mobile devices could potentially turn everyone into a mapmaker Collaborative mapping is an initiative to collectively produce models of real-world locations online that people can then access and use to virtually annotate locations in space This chapter describes the technical and social developments that underpin this revolution in mapmaking It presents a framework for an alternative geographic information infrastructure that draws from collaborative mapping initiatives and builds on established Web technologies Storing geographic information in machine-readable formats and exchanging geographic information through Web services, collaborative mapping may enable the “napsterisation” of geographic information, thus providing complementary and alternative geographic information from the products created by national mapping agencies Introduct ion Since the Enlightenment, mapping and the production of geographic information have been institutionalised: the map is the power At home, maps were used as an instrument for nation building as nation states emerged: a legitimisation device (McHaffie, 1995) People learned about their country and administrations needed a tool to govern the territory Away from home, maps were an instrument for colonisation, when Africa and Asia were split among the European nation-states During the last few decades, there has been rapid democratisation of geographic information and maps Sawicki and Craig (1996) distinguish Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited Collaborative Mapping and GIS three ways in which this movement is apparent First, the locus of computing power and data access is broadening Second, the level of skills to turn raw geospatial data into geographic information has become less demanding Third, the locus of applications has moved closer to the citizenry Geographic information systems moved from mainframes and the UNIX operating system onto personal computers and the Windows operating system From research and government, GIS spread into the business sector The PARC Xerox Map Server and Virtual Tourist brought maps to everyone’s PC in the late 1990s, followed by online map Web sites such as MapQuest and Multimap In 1997, Brandon Plewe noted that “the Internet holds promise for exponential increases in the efficiency and effectiveness of the ways in which we obtain, use and share geographic information in all its forms” (Plewe, 1997) In July 2002, 7.1 million European users visited one of the many online map Web sites (Nielsen//NetRatings, 2002) Google Maps, introduced in February 2005, reached almost 1.7 million visitors in that month (Buchwalter, 2005) Although maps are more widely used than ever, the production of geographic information, and especially mapping, is still highly concentrated among national mapping agencies and the GI industry But this oligarchy is soon to be dissolved, for we see the third aspect of the democratisation of geographic information–the locus of applications moving closer to the citizenry–becomes apparent now that location-aware mobile devices are coming within everyone’s reach GPS units are not only available to surveyors anymore, as cheaper devices are sold for outdoor recreation Also, small GPS antennae can communicate with other devices over Bluetooth, and there are already mobile phones and personal digital assistants (PDAs) for the consumer market that have GPS-chips built in At the same time, digital maps have become portable Various mobile phone operators have started to deliver location-based services to mobile devices Mobile phones come with route planning applications, thus making in-car navigation systems redundant Maps are not only delivered to the desktop, but also to mobile phones and PDAs, requiring new visualisations as the screen size, resolution, and use patterns differ significantly Collaborative mapping is an initiative to collectively create models of real-world locations online that anyone can access and use to virtually annotate locations in space (McClellan, 2003) The value of the annotations is determined by physical and social proximity, the former expressed in distance, the latter in “degrees of separation.” Thus, the informational value and the pertinence of spatial annotations is not only dependent on physical distance, but also dependent on the trust relationship between individuals or groups of people through social networks: the “Web of Trust” (Espinoza, Persson, Sandin, Nystrom, Cacciatore, & Bylund, 2001) However, there is a discrepancy between physical and social proximity Privacy and personal freedom become highly important issues when one’s location is related to their social behaviour On the other hand, the fear of surveillance that accompanies positioning is already gradually reducing in society (Ahas & Mark, 2005) Furthermore, this discrepancy can be mediated by users themselves by storing annotations and tracks locally, thus creating distributed repositories, and by explicitly setting the level of privacy on each of these annotations and tracks Finally, users themselves remain in control of their social identification–their preferences and social network–while they make use of collaborative mapping services, whereas, for example, the social positioning method aggregates these social characteristics to study the space-time behaviour of society (Ahas & Mark, 2005) Collaborative mapping services are therefore less pervasive in the privacy of their users because users negotiate the trade-off between the benefits of the service and their privacy concerns 389 Semantic Interoperability of Geospatial Services The full XML serialization is not shown here due to space limitations In the example, the daml:collection represents a DAML+OIL extension of RDF to provide a “shorthand” representation of structured list that defines triples It should be noted that the daml:Class rdf:ID=”ManagementAgency” is derived from an external URI of Opencyc (OpenCyc, 2006) to illustrate inheritance from a generic class Using the set of axioms provided by DAML+OIL, one can assert class subsumption, equivalence of class or property, and various constructors such as intersectionOf, and Maxcardinality to create complex semantic constraints An intelligent agent with an inference engine can easily deduce new knowledge about the environment from the ontology E xplorative Land U se C hange Analysis: Simulations of Multi-Agent Interaction Given ontology, multiple agents can interact meaningfully The following example illustrates an agent’s understanding of the domain model that can be used to simulate different disicion scenarios visualized by map rendering A “whatif” type of pollution simulation model has been developed in response to the decision of changing land use from one category to another category (see Figure 4) This model can be used to simulate the consequence of an agent’s decision, when for example, an agent changes a certain category of land use from “vacant” to “landfill.” The result is a corresponding increase or decrease in pollution content Every request to change in land use type results in recalculation of the mass export of pollutant and corresponding statistics The resulting pollution map can be visualized with multiple theme overlay The system logs individual user’s preferences to input into mediating algorithm to resolve conflict among user preferences of land use choice Built on top of the ESRI’s ArcIMS, the system uses ArcXML (native XML encoding of spatial object) to communicate between the custom middleware and the Web mapping server The services offered by the Web mapping server are similar to the requirements of OGC’s implementation specification for a Web map service (WMS) The map server contains registered model components which are instantiated following a request from an agent The server side application processes the agent’s request and makes necessary updates in the database to reflect the corresponding changes of the pollutant coefficients Every request to change in land use type results in recalculation of the mass export of pollutants and corresponding statistics The processed result is then sent back to the Web server and then to the client agents For a given planning zone, the environmental regulation of land use constraints is stipulated by the management agency The domain ontology includes several such constraints: Zone (low _ density _ residential) PermittedL andUseCategory(multifamily _ dwelling) ∀x,y∃d HighDensityResZone(x)^PreservationZone (y)MinDistApart((x,y),d) Similarly, other spatial contiguity constraints or topologic constraints can be expressed in domain ontology in relation to the neighboring zone or land use The system provides cartographic rendering options for typical mapping manipulation procedures such as selecting and identifying a feature, visual queries, rendering legends corresponding to feature types (classification for 417 Semantic Interoperability of Geospatial Services Figure Implementing map service for simulation of land use change both continuous and unique data type), multiple themes, and overlays The solution space of the explorative scenario generated by the simulation process can be further resolved using different a mediation algorithm in an evolutionary process For instance, genetic algorithms can be used to construct links between an agent’s template and a possible solution space and let the system evolve until a mutually acceptable solution converges C onc lus ion In this article, how the use of semantic reasoning can be used to aggregate and reason over geographic features taken from multiple sources has been demonstrated It has been noted that a semantic layer is essential to fulfill the objectives of e-government’s geospatial portal To the best of knowledge, there is no current standard specification for re-use of spatial models which could enable high level effective communication among different agents There is still a need for a generic formalism to link geo-spatial models to the domain specific application classes Using 418 a multi-agent paradigm, a promising direction to solve complex spatial problems that would be otherwise difficult to solve has been demonstrated The OSIRIS framework holds that the description of the infrastructure of spatial data is essential for ontology-assisted interoperability of heterogeneous sources It has been demonstrated that ontology can be used to provide a common context for the semantic grounding for spatial application models which agents can relate to their native terminologies and thereby enable access to multiple services Spatial services can be composed or manipulated along with other type of services, including Web services The implementation of the sample prototype of OSIRIS framework indicates that a scaleable Web based spatial ontology can be developed using ontology modeling language to enable real world spatial decision-making However, further research is needed to understand the requirements of ontology language to represent the semantic structure for complex spatial and temporal objects This is especially important for ontology matching and merging of complex spatial systems In particular, the implication of imprecision related to Semantic Interoperability of Geospatial Services the finite resolution 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Journal of Intelligent Information Technologies, Vol 4, Issue 1, edited by V Sugumaran, pp 31-51, copyright 2008 by IGI Publishing (an imprint of IGI Global) 421 422 Chapter XLVII Biometric Authentication in Broadband Networks for Location-Based Services Stelios C A Thomopoulos National Center of Scientific Research “Demokritos,” Greece Nikolaos Argyreas National Center of Scientific Research “Demokritos,” Greece Abstr act Broadband communication networks have begun to spread rapidly over fixed networks, with wireless networks following at close distance The excess capacity allows the offering of broadband services at competitive rates Location-based services (LBS) over wireless broadband networks are becoming mainstream in an emerging ambient intelligence society For LBS over broadband and, in particular, pier-to-pier networks, such as ad hoc networks, unambiguous user authentication is of paramount importance to user trust and safety, thus ultimately to the success of such service Biometric authentication is an approach to providing irrefutable identity verification of a user, thus providing the highest level of security This chapter addresses some of the issues associated with the use of biometric ID for user and apparatus authentication over broadband wireless networks (e.g., GPRS, UMTS, WiFi, LANs) and narrow band local networks (e.g., bluetooth, Zigbee, PANs, BANs) INTRODUCT ION The spreading of broadband networks stimulates a wealth of Internet services over fixed and wireless networks with stationary and mobile devices Combining accurate location information from enhanced GPS infrastructures, such as EGNOS, Galileo …, with broadband wireless networks, provide the necessary infrastructure for delivering high quality and versatile location-based services Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited Biometric Authentication in Broadband Networks for Location-Based Services (LBSs) ranging from travel information to entertainment, to crisis and incident management, to services on demand, to health care and peer-topeer communications, to mention just a few In all these services, the common thread is the ability to unambiguously identify and authenticate the mobile user and customer to the LBS provider Different LB services may have different authentication requirements However, no matter what the application is all such services, the unambiguous authentication of the user is paramount to gaining the trust of the end user and thus achieving the success of the services Unambiguous user authentication is paramount to the parties involved in an LB service and the trust upon which the service is built If for example the LBS refers to the provision of transport services on demand, the ability to correctly identify and authenticate both parties involved in the transaction, that is the passenger (i.e., the user) and the driver (i.e., the service provider) build mutual trust and can be proved life-saving in the case of a car-jacking, criminal activity, or fraud User identification and authentication can be performed by a variety of means, ranging from a simple alphanumerical password to a more secure digital signature, to the ultimate in security biometric ID Although a digital signature produced by an electronic device provides the convenience of a self-contained identification instrument, it does not prevent fraudulent use of a user ID Since there is no unique and inherited connection between the user and the digital ID, any holder of the electronic device that produces the digital ID can produce a fraudulent authentication The only means to eliminate such possibility is the use of biometric ID Biometric ID is a digital signature generated from the measurement of some bodily human characteristics that are unique, or different enough to be considered unique, from user to user This Biometric ID, encoded properly, constitutes a unique signature for each user that cannot replicated by an impostor This biometric ID can be used to meet the stringent requirements imposed by LB services and the necessary trust required by users and operators of such services alike Examples of biometrics commonly used for user identification and verification include fingerprint identification, iris scan, face and voice recognition, signature recognition, hand geometry, and combinations thereof (Reisman & Thomopoulos, 1998; Thomopoulos & Reisman, 1993) The use of biometric ID imposes certain restrictions and technological challenges that need to be addressed before biometric authentication becomes widely used as an enabling technology for irrefutable user authentication in LBS and other broadband services B IO METR IC ID A Requirements for Biometric ID U sage Biometric ID is the mathematical encoding of certain bodily features that are considered unique for each human being and differ enough from person to person so that this difference can be used safely enough to tell apart one person from another For example, in the case of fingerprints, biometric features are the characteristic points that are formed from the endings or bifurcations of the finger ridges and/or the pattern of the ridges themselves In the case of the iris scan, biometric features are the radial patterns of the iris In the case of the face, biometric features are the relative location of the eyes, mouth, nose, and so forth The mathematical encoding of the biometric features constitutes what is referred to as biometric “template” or biometric ID (Thomopoulos, Reisman, & Papelis, 1996) No matter which biometric is used, the biometric features constitute unique and personal human characteristic and as such, they are protected by the Personal Information Protection Act (PIPA) (Personal Information Protection Act, S.A 2003, c P-6.5) This protec- 423 Biometric Authentication in Broadband Networks for Location-Based Services tion imposes a number of issues, concerns, and restrictions with the extraction (or capturing) of biometric features, their (electronic) storage, encoding into a biometric template and the subsequent retrieval and use of this template for user authentication In addition to privacy, other concerns with the use of biometric features relate to the medical information that may be contained in and revealed by biometric features, the safety of the process and devices used in the extraction and verification process, and the protection of the user’s privacy (ANSI:The American National Standards Institute, 2005; also ISO: International Organization for Standardization) A biometric “template” constitutes a nonreversible mathematical transformation of the extracted biometric features (Thomopoulos, Reisman, & Papelis, 1996) In that respect, it cannot be used outside the specific device that is used to extract it (and subsequently verify it) as a means to identify one’s identity without his or her consent, thus violating the PICA However, it can be used indirectly through the use of the specific device and process used to extract it to coerce evidence about one’s identity without his or her consent In that regard, even the biometric template, albeit its mathematical irreversibility, constitutes private and personal data and as such it is mandatory that it is protected by the Private Information Protection Act (PIPA, S.A 2003, c P-6.5) The restrictions imposed by the PICA reflect on the technological specifications for the processes and devices used for biometric ID verification Hence, the requirement that any biometric information, including the biometric template, constitutes private data and requires that the biometric data be stored in a memory device that is only accessible by the owner of this data, either in the form of raw biometric data or a processed biometric template (Stapleton, 2003) Furthermore, any retrieval of such information from the storage device (memory) must be done 424 over secure and cryptographically protected communication links, and any subsequent processing of this information must be done by a secure processor These restrictions lead to almost uniquely defined architectures for biometric verification (The BioAPI™ Consortium, 2005) The most readily available and seemingly universal architecture involves the use of a smart card as a memory device for storing the user biometric ID template and a local secure processor for capturing the biometric information, processing it to extract the biometric features, and comparing it against the stored template Alternative storage technologies include memory chips or tokens In this case, the biometrics data processor is part of the memory device, in a size that is not larger than a conventional memory stick Using either technology, the PICA requirements can be met The biometric template is always at the possession of the user, any processing is done locally, and over links and processors that are securely isolated from the WWW However, in the process of enforcing PICA, one of the major advantages of biometrics and biometric ID, namely the convenience not to carry any type of ID other than one’s self, is being lost This is the trade-off between the convenience offered by the card-less biometric identification, and the restrictions imposed by PICA that translate in the need to carry and use a plastic card in order to store the biometrics template in a user-controlled memory The cartoon displays in Figures and show the typical registration and authentication processes for biometric ID These processes have been implemented in the VeriEasy biometrics access control system that was developed in the context of the Bioathletics1 project The project aims at implementing a distributed biometrics ID system to allow secure access to spectators, VIPs and the press in athletic installations and events using their biometric ID only Biometric Authentication in Broadband Networks for Location-Based Services Figure Registration process of the PICA-compliant smart card based biometrics (fingerprint) VeriEasy access control system for athletic events and installations Bioathletics Biometrics Access Control System: Registration Biometric Fingerprint Card Reader Spe ctat or Op era Biometrics Access Rights Administration db tor Athle ti Even c ts Regis tratio db n Smart Card for Storing Fingerprint Template Wiegand Reader B IO METR ICS IN ATH LET ICS Athletic events represent an ideal field of application and showcase test beds for biometrics as they combine a usually large crowd, the need for speedy but secure authentication, electronic ticketing, and overall security concerns by potential troublemakers VeriEasy™ was developed in the context of the funded research program Bioathletics to provide a unified BioAPI compliant platform for addressing the concerns of the athletic events industry in secure physical access to stadium for athletes, spectators, and VIPs In the VeriEasy™ system, the biometrics (fingerprint) template is stored directly from the card Verification and Access Process as implemented in the VeriEasy access control system for athletic events and installations reader in a smart card that stays with the template’s owner at all times This way the PICA requirements are fulfilled in terms of both privacy and protection against eavesdropping, since the template stays with his or her owner at all times and processing is done locally over a trusted link The user smart card serial number is then linked to a back office legacy system database that contains demographic (non-private and sensitive) information about the user and access rights and athletic event attendance information During an athletic event, the user accesses the stadium or the equivalent athletic installation using his or her biometric ID smart card The stored user’s biometric ID is compared against his or her fingerprint live scan locally using the same secure local processor and communication link If the authentication is positive, the system is connected via a broadband connection to the back office athletic events database to acquire the appropriate access permissions for the specific event the user is attempting to gain access to If the user has purchased the appropriate ticket for the event or has access rights to it, an authentication acknowledgment signal is returned to the device to enable access to the athletic premise Alternative, a positive acknowledgment message can be displayed on a monitor, if the system operates with a human attendant The verification and access process for VeriEasy is shown in a cartoon way in Figure Another restriction that is imposed on biometrics ID systems is the encryption of any bio- 425 Biometric Authentication in Broadband Networks for Location-Based Services Figure PICA-compliant smart card based biometrics (fingerprint) Bioathletics Biometrics Access Control System: Verification Biometric Fingerprint Card Reader Access Rights Controller Doo Contr r oller Athletic Events Registration db metrics data Encryption in the VeriEasy system is achieved using a private key that is encrypted in the smart card Other encryption methods are possible and can be used in conjunction with the smart card encryption Another link that must be secured in the Verification and Access process is the physical electric connection from the door controller to the electric door latch This link can be secured by using an encryption-decryption hardware pair to encrypt the electric signal that commands the opening and closing of the gate This way the entire local biometric authentication-access process is secured It remains, however, to secure all long haul communication network links In order to make the biometrics ID registration-verification-access process entirely secure globally, all long hauls fixed or wireless communication network links must be secured as well One way to achieve this is use VPN tunneling for data communication from the distributed access points to the centralized back offices database or databases that hold the athletic event schedules, ticket information, event schedules, athletic installations layout, access rights, and authorizations Finally, if the biometric authentication devices communicate with the local Access Rights Con- 426 troller via a bluetooth or zigbee wireless link, this wireless link must be secured as well Both bluetooth and zigbee protocols provide a security layer but the level of security of these layers is still not widely tested Hence, if a bluetooth or zigbee wireless link is used in the biometrics ID system, it may be required to use encryption on top of their security layer until their security is extensively tested and proven Figure depicts the VeriEasy verification process as a generic secure verification process applicable to any biometrics ID authentication system operating over any broadband communication network Hence, the biometrics authentication system architecture of VeriEasy, and thus Bioathletics, provides: a trusted and secure means of biometrics ID extraction and processing at the local level, compliant with the PICA specifications for personal data protection and privacy; a globally secure network architecture for data exchange and information communication; and a locally secure wireless network for the interconnection and networking of devices and signaling Any biometrics authentication system since the biometrics field is continually evolving and no standard has been reached yet Biometric Authentication in Broadband Networks for Location-Based Services Figure Securing all communication links, fixed and wireless, either via a VPN, bluetooth, or zibgee, the security layer provides a globally secure and trusted, PICA-compliant smart card based biometrics (fingerprint) authentication for any broadband application with emphasis to LBS applications Card Reader Controller Smart Card sftp // v pn Internet Verifus ed Physically Protect n Wired Connectio Sitekey Door Controller sftp // v pn Wireless LAN with  bit WPA Security Internal closed communication at hardware level The Bioathletics paradigm can be generalized to other broadband applications that require biometrics ID authentication A number of useful conclusions drawn from the Bioathletics project are summarized in Table These conclusions are relevant and applicable to the implementation of any PICA-compliant biometrics ID authentication system that may be required for the unambiguous and trusted user authentication in broadband applications, with LBS being one of them In addition to the security requirements imposed on a biometrics ID authentication system, the system must also be Bio-API compliant Compliance with the Bio-API allows to encapsulate all manufacturer dependent biometric and algorithm processes in callable DLLs In this way, the core structure of the biometrics ID authentication system remains fixed, whereas new biometric feature extraction, encoding, and verification modules can be easily integrated in the system with minor modifications This modularity is extremely important as it allows adaptation to new biometrics Net-Technologies DB as time progresses This adaptation is keen to the success of any biometrics authentication system since the biometrics field is continually evolving and standards have yet to be defined B ioAPI™ and BSP Implementation The BioAPI™ Specification version 1.1 is an ANSI (INCITS 358-2002) standard and currently under ISO standardization process (ISO/IEC JTC1/SC37) specifying an Application Programming Interface (API), which was introduced to facilitate the implementation of Biometric applications (The BioAPI Consortium; and The BioAPI Consortium: BioAPI™ Specification version 1.1) BioAPI is a standard biometric technology interface, which is intended to provide a high-level generic biometric authentication model; one suited for any form of biometric technology BioAPI supports, among other lower level functions, the basic functions of Enrollment, Verification, and Identification, and includes a database interface 427 428 Biometrics reader with local processor Local with the user: Smart Card or Memory Stick with or w/out processor and built-in memory Biometrics reader with local processor & Local Card or Memory Stick reader Secure Server Biometric readers, controllers, actuators Biometrics template storage Biometrics ID authentication Access of back office permission rights db Local devices and command signaling via wireless links Device Biometrics feature extraction and template creation Process Wireless local links: bluetooth or zigbee VPN with or without additional data encryption Local Encryption of data may be required from smart card or memory stick to local processor Directly in a smart card or memory stick Local and secure Data encryption if necessary Communication Link bluetooth or zigbee Security protocol with or w/out additional data encryption Secure Secure and trusted Private and secure Secure and trusted Security level Secure personal data transfer if inevitable N/A unless it carries private data info – to be avoided Privacy and protection of personal data Privacy and protection of personal data Personal data protection PICA requirements Yes N/A Yes Yes Yes PICA compliant Biometric Authentication in Broadband Networks for Location-Based Services Table Recommendations for the implementation of a biometrics ID authentication system Biometric Authentication in Broadband Networks for Location-Based Services to allow a biometric service provider (BSP) to manage the Identification population for optimum performance in an application independent manner There is a reference implementation for Microsoft® Windows® (early alpha) publicly available by the BioAPI™ Consortium 0, as well as two internally released beta implementations; one for Java (under Windows®, via JNI) by (Gens Software Ltd); and one for Unix™ / Linux™ by SAFLINK There are several commercial implementations to date—mainly by members of the BioAPI™ Consortium—either in the form of BioAPI™ framework SDK or in the form of BSP Biometric Service Provider (BSP) SDK or specific BSP implementations for the vendors’ Biometric Devices However, market acceptance is still immature and market penetration of BioAPI™ compliant applications and biometric devices is speculated to significantly increase in the next two years (The BioAPI Consortium; and The BioAPI Consortium: BioAPI™ Specification version 1.1) A BioAPI™ compliant application consists of at least two different modules, the application module, which could be seen as the “top” level application (Level H) and the BSP (Biometric Service Provider) module, which could be seen as the “bottom” level application (Level L) The application module would be the biometric application and the BSP module would be the service provider module, which should control the biometric hardware The BSP could be seen as a black box from the side of the application developer, since the technical details of a given device will not concern him Access to the biometric mechanisms is through a set of standard interfaces defined by BioAPI™ The approach taken by the BioAPI™ is to hide, to the degree possible, the unique aspects of individual biometric technologies, and particular vendor implementations, products, and devices, while providing a high-level abstraction that can be used within a number of potential software applications One of the most important terms of the BioAPI™ standard is the BIR The term Biometric Identification Record (BIR) refers to any biometric data that is returned to the application; including raw data, intermediate data, and processed sample ready for verification or identification, as well as enrollment data Theoretically, BSPs that are supplied by vendors and conform to the BioAPI™ interface specification, can be used by any application developed using the BioAPI™ framework The BioAPI™ Consortium claims, among other things, that the BioAPI™ will enable rapid development of application, flexible deployment between platforms, and simple application interfaces modularity of biometric applications For an application to be compliant with the BioAPI™ specification, a software application must perform that operation consistent with the BioAPI™ specification for each BioAPI™ function call made That is, all input parameters must be present and valid There is no minimum set of functions that must be called For a BSP to claim compliance to the BioAPI™ specification, it must implement mandatory functions for their category They are categorized as either a Verification or Identification BSP BSPs must accept all valid input parameters and return valid outputs Additionally, they must provide all required module registry entries Entries to the module registry must be performed upon BSP installation Biometric data generated by the BSP must conform to the data structures defined in BioAPI™ Specification A BioAPI compliant BSP must support all Module Management and Handle operations When an application is connected to a given BSP, the process initializes the BioAPI™ Framework, loads the BSP desired, and then attaches a module of the given BSP to the application Once the application is connected to a BSP, it can perform BioAPI™ calls for several biometric operations as Capture, Enrollment, Verification, and Identification 429 Biometric Authentication in Broadband Networks for Location-Based Services The BioAPI™ concept is very interesting, but some things in the implementation may get confusing For instance, data exchange between the BSP and the application can be an issue There is no asynchronous way to pass data from the BSP to the application Also when it comes to communication of the application and the BSP, the rules of communication can be quite restraining In the given implementation of BioAPI™, when it comes to the BSP wanting to notify the application that some event has occurred, the API defines that the notifications that can be made are “on insertion” or “on removal” of a biometric device, “on fault” of a device and “on presence” or “on removal” of a biometric source (e.g., presence of a finger on a fingerprint device) In other words an application can only be notified if: a A new device is plugged in the system b An existing device is unplugged from the system c An existing device doesn’t work properly d A biometric source is presented to one of the biometric devices in the system, or is removed from it The application can command the BSP with commands such as BioAPI_Enroll(…) or BioAPI_Verify(…) The idea of BioAPI™ includes the idea that any attached device should be handled from the BSP so that different devices can be used if they provide a suitable BSP However, as existing biometric devices may not be fully compliant to BioAPI™, there might be cases that it may be deemed necessary to command the device to perform something that is not foreseen by the BioAPI™ without violating the BioAPI™ standard Otherwise, the application will no longer be BioAPI™ compliant, since no other de vice is expected to work with the same command and the idea of the BSP module as a black box is lost Another problem exists with the data exchange between the application and the BSP The API gives the application the right to use a payload as input on Enrollment and Create Template, or as 430 output on Verification and Verify Match (Identification) In any other case the API does not provide the application the right to exchange data with the BSP Two questions are raised then: a What happens in the case that the application needs to send data to the BSP asynchronously and vise-versa? b What should the payload data be, if the data that is exchanged through payload are not specified by the API? The data exchange through payload might be anything, but since the API does not define what this data should be, then it is almost impossible to find two different BSPs passing the same data through payload and on the same form This means that the application is highly impossible to be able to function with the BSP of a different device, thus a need for BSPs for devices from different manufacturers and vendors Ex pand ing the c apab ilit ies of V er iEa sy™ In many applications, in particular in high security/high authentication fidelity cases, more than one biometrics technology is required to exist and operate under different authentication scenarios in the same environment Such a heterogeneous biometric devices network may consist of fingerprint scanners, voice recognition devices, and face and iris scanners, all working in various combinations for ID verification under various authentication scenarios Using the VeriEasy™ client-server architecture, we can build BSPs for each type of biometrics device we intend to use in the security network and control them via a single application! This way the highest security standards are achieved without loosing any flexibility For example, in a heterogeneous biometrics network, the Server BSP can grab the proper fingerprint template from the smartcard or Biometric Authentication in Broadband Networks for Location-Based Services request to the application for the right one when a centralized Template Database is in place The obvious advantage of this architecture is that it defines a middle layer between the application and the native BSPs, so that the native BSP programmer needs to know about his device and nothing more, the middle layer programmer (if any) needs to know about the biometric network and nothing more, and the application programmer needs to know nothing at all about biometrics! Furthermore we can use plug and play BSPs (e.g., have a BSP installed, as a client one), and let the Server BSP to reconfigure itself in order to handle the new device MU LT IFUNCT ION AL BSP s Using more than one biometric technology may be a highly unprofitable venture, because of the cost of different kinds of devices coming from different hardware distributors But even more, the maintenance of such a system could be proved a constant headache for our administration personnel (different contracts of maintenance) It is here where VeriEasy™ comes to provide the winning solution the multifunctional BSP That means that the same device can operate in many different ways so it is up to us to choose the proper way for the specific device, whenever we want to Hence, setting up the most complex biometric devices network to meet the security needs of an organization is only one click away! Moreover, if for security concerns changes are required in the security network design at run time (!), VeriEasy™ allows on the fly reconfiguration of the security network design without service interruption! Biometrics Authetication in B roadband N etworks and Location-B ased S ervices Advances in broadband, and in particular wireless broadband networks, enable the provision of personalized multimedia services, as well as location-based services (LBS’s) Those services extend from electronic maps and navigation, to location sensitive tourist guidance, transportation, car-pooling, ad hoc networking, and ubiquitous computing, to incident management, emergency services, to make reference to just a few In all LB services, the sensitive information is the user’s location and the user’s identity Both these two pieces of information must be protected by the provided service or infrastructure the service is running on, and authenticated when necessary by means directly controlled by the user Simply imagine how critical it is to authenticate the user’s ID unambiguously when the LBS is a car pooling service provided over an ad hoc (peer-to-peer) network In such an application where verification of the true identity of the user may be a life and death situation, it becomes imperative to have a bullet-proof biometrics authentication system to provide unambiguous user ID verification Such a system should protect the user ID from theft, forgery, and misuse C onc lus ion The architecture and design of a distributed biometric ID verification system has been presented The VeriEasy™ system provides protection of the user privacy and ID, compliant with the sensitive personal data protection guidelines and legislations, and is BioAPI compliant using BSP compliant interfaces Acknow ledg ment The research for the development of a BioAPI™/ BSP compliant biometrics authentication system for ID verification in athletics events have been sponsored by the General Secretariat of Research & Technology, of the Greek Ministry of Development, under Contract No ΣΠ/ΑΘ/17+32, Operational Program “Competitiveness” Oct 2003 431 ... xmlns:rdf =”http://www.w3.org/ 199 9/02/22-rdf- syntax-ns#” x mlns:d a ml=”http://w w w.d a ml.org/2001/03/ daml+oil#” xmlns:rdfs=”http://www.w3.org/TR/ 199 9/PR-rdfschema- 199 90303#”>