Technology behind Aadhaar. Unique Identification Authority of India Tampa, 20 th September 2012

Transcription

1 Technology behind Aadhaar Unique Identification Authority of India Tampa, 20 th September 2012

2 Aadhaar at a Glance Technology Strategy Architecture Enrollment Process Status Authentication Fingerprint PoC Iris Poc Conclusions Agenda

3 Aadhaar at a Glance Technology Strategy Architecture Enrollment Process Status Authentication Fingerprint PoC Iris Poc Conclusions

4 1.2 billion residents India 640,000 villages, ~60% lives under $2/day ~75% literacy, <3% pays Income Tax, <20% banking ~800 million mobile, ~ mn migrant workers Govt. spends about $60 bn annually on direct subsidies/payments 4

5 Vision Create a common national identity for every resident Biometric backed identity to eliminate duplicates Verifiable online identity for portability Applications ecosystem using open APIs Aadhaar enabled bank account and payment platform Aadhaar enabled electronic, paperless KYC 5

6 Issue unique IDs UID Unique number Random number Name Parents Gender DoB PoB Address Basic demographic data and biometrics stored centrally Standardized identity attributes UID = No duplicates(1:n check) Flexibility to partners on Know Your Resident (KYR)+ Central UID database Property of UIDAI - Highly confidential 6

7 and authenticate IDs online, real-time Authentication - Are you who you claim to be? UID = Central UID database 1:1 check, no ID fraud Only YES/NO response, no details no invasion of privacy Person can see self-details, no one else can 7

8 Context of UIDs in India Technology Strategy Architecture Enrollment Process Status Authentication Fingerprint PoC Iris Poc Conclusions

9 Consultation & Standards Biometric Standards Demographic data standards and verification procedure Process (90 days) Representation from agencies, academic and industry Standardization on modalities and data formats

10 PoC to Determine Enrollment Process Three states, 10s of villages Rural areas emphasized Data collected in 2 sessions from 75K people Capture time is 4 min. Spread is 50% Social customs are not a major problem Zero FTE is possible De-duplication of 1.2 B possible through 10 finger prints and dual irises

11 Biometric Strategy Multi-modal: Improve de-duplication accuracy using multiple modalities 10 Fingerprint, 2 Iris, Face Multi-Vendor Risk Mitigation No Vendor Lock-in Vendors compete for volume allocation Performance Accuracy

12 Overall Strategy Best of breed through standards & open source Sourcing from multiple suppliers Leverage market forces for technology improvement Create national standards wherever necessary through extensive consultation Build eco-system Device certification Operator certification Empanelment of enrollment agencies IT and other suppliers training for state level reengineering apps Conduct field test to validate assumptions

13 Technology Stack Multi-platform client All 3 rd party interfaces abstracted through standard API layer (VDM, ABIS, Language Support, Linux with virtualization at OS layer MySQL as RDBMS Java application Apache Hadoop (HDFS, Hive, Pig, etc.) stack for large scale compute and distributed storage RabbitMQ (AMQP standard) as messaging framework Drools for rules engine Several other open source libraries

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16 Architecture Principles Design for scale Every component needs to scale to large volumes Millions of transactions and billions of records Accommodate failure and design for recovery Open architecture Use of open standards to ensure interoperability Allow the ecosystem to build libraries to standard APIs Use of open-source technologies wherever prudent Security End to end security of resident data Use of open source 16 Data privacy handling (API and data anonymization)

17 Designed for Scale Horizontal scalability for all components Open Scale-out is the key Distributed computing on commodity hardware Distributed data store and data partitioning Horizontal scaling of data store a must! Use of right data store for right purpose No single point of bottleneck for scaling Asynchronous processing throughout the system Allows loose coupling various components Allows independent component level scaling 17

18 Aadhaar Services Open Architecture Core Authentication API and supporting Best Finger Detection, OTP Request APIs New services being built on top Aadhaar Open Standards for Plug-n-play Biometric Device API Biometric SDK API Biometric Identification System API Transliteration API for Indian Languages 18

19 Open Standards & specs Open Source Biometric Standards UID Specifications Hadoop ISO X Enrolment Device HBase CBEFF Authentication Device MySQL MINEX Mongo DB IREX RabbitMQ PIV - FP BI: Hive

20 Security & Data Privacy Encryption of Enrollment Packet Decrypted packet never stored on disk Biometric images archived logically offline Data anonymized from ABIS vendors Only store templates and not raw images Data Centre Security DMZ, firewalls, IDS, IPS

22 Enrollment Process

23 Enrollment process Demographic Data Compulsory data: Name, Age/Date of Birth, Gender and Address of the resident. Conditional data: Parents/Guardian details Optional data: Phone no., address Biometric Data Resident s Photograph Resident s Finger Prints Resident s Iris # Provision of Exception handling 23

24 NOC for Enrolment Monitoring

25 UID Middleware Standardization of the ABIS interface Highly distributed, concurrent, fault tolerant architecture Continuous unit and accuracy testing on the production system Test using real data (probes representative) No information is provided to ABISs to distinguish probes from real data Continuous testing of data integrity System management, monitoring and diagnostics

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27 99.943%

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29 Enrolment Volume 600 to 800 million UIDs in 4 years 1 million a day 200+ trillion matches every day!!! ~5MB per resident Maps to about PB of raw data (2048-bit PKI encrypted!) About 30 TB I/O daily Replication and backup across DCs of about 5+ TB of incremental data every day Lifecycle updates & new enrolments will continue for ever Additional process data Several million events on an average moving through async channels (some persistent and some transient) Needing complete update and insert guarantees across data stores 29

30 Enrolments happening all over the country Represents geographies with registered stations 60,000+ active Enrolment stations 60+ registrars - State Governments, Banks, India Post, Financial Institutions etc

31 Enrollment Devices Today Cost reduction Enrollment Station > 50% price reduction to $2,000 Slap scanner and Dual eye Iris camera From >$2,000 to $600 average Zero FTE is achievable Device innovation Hot swappable, UNIX/Windows support

32 Enrollment Quality - Definitions Methodology Quality metrics embedded in enrollment packet Face: ICAO-- (slightly relaxed) FP: Poor quality when there is at least one finger with NFIQ >3 in each of three slaps (4, 4, 2) Iris: Poor quality when Irisness score < 50 (proprietary)

33 Enrolment Quality - Results Govt. Policy - everyone must be enrolled ie FTE=0% Biometric FTE: 0.14% (no FP & Iris captured) Poor Quality FP & Iris: 0.23% Poor Quality FP: 2.9%, Iris: 3.0%

34 Analysis & Interpretation Multiple modality improves FTE by 10 to 25x Quality is comparable to Western results despite Diverse demographic Effect of manual labor (FP) Good biometric obtainable from 5 yrs age Senior population difficult but still feasible

35 Multi ABIS Multimodal Results FPIR Probe size: 4M False rejects: 2,309 FNIR Probe size: 32,000 False accept: 11 FPIR: 0.057% FNIR: Gallery = 84 Million NIST 7112 Ten FP Results FPIR: Gallery= 1 Million Multiple modality provides similar accuracy for 100X larger gallery

36 De-duplication Conclusion Competitive advantage of using 3 ABIS & SDKs Continuous FPIR/FNIR measurements Possible to maintain low FPIR/FNIR over wide range of gallery size

38 Authentication YES Name, gender, DoB, Age, Address, Mobile, , OR NO 38

39 Why is Biometric Authentication challenging? Inclusiveness Can t deny benefits. Diverse subjects Manual labor Senior and children benefit programs Interoperability under open architecture Enrollment done using 11 different devices 30+ single FP scanners & extractors 8+ iris mobile cameras Mobile GPRS network Variety of applications 1 st in the world to operate on-line Auth.

40 Thumb: Enrollment & Verification Slap Scanner for enrolment Single Finger Auth Device

41 9 PoC over 12 months across India 50,000+ subjects Study Coverage or FTE Devices # of fingers, # of eyes Image quality Demography Network, mobility Proof of Concepts

42 Proof of Concept Scenario Conducted in the real field environment Real subjects representing local demography Production system & network Technology 17 distinct scanner models Every resident verifies on ALL devices Images captured at source 3,000 subjects Best Finger Detection - BFD

43 Throughput Performance 10 million authentications in 10 hours Average response time around 200 milliseconds or 295 concurrent requests/sec. Performance test environment consisted of 15 blade servers including database servers, biometric matching servers, messaging server, caching servers, and audit logging servers. Configuration: x86 Linux dual CPU 6-core.

44 FP Conclusions Achievable Accuracy (for 98.2% of population) FRR < 1% with two best finger fusion FRR < 2.5% with one best finger Device Certification More selective devices improve FRR by 2X Placement guide can also improve FRR materially PIV compliance insufficient indicator FAP 20 very useful Field accuracy test should be part of device certification Throughput of 1M/hr is easily achievable

46 Proof of Concept- iris Objective- the feasibility of using iris modality for online authentication Coverage/ Accuracy/Readiness Set-up 4 single eye, 4 dual-eye cameras Every resident verifies on ALL devices Production system & network 5,000 subjects semi-rural location Poc X Seniors Mysore years years 66 and above India % 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

47 Coverage Single-eye cameras Authenticated in first try Dual-eye cameras Authenticated in multiple tries Failed (FTC+FRR) Over 99.5% population coverage is possible for on-line iris authentication

48 50% of Failures (FRR, FTC) due to Intra Capsular Cataract Extraction (ICCE) & Other types of surgery Special eye conditions

49 Accuracy Auth mode Single eye camera Dual eye camera Single eye 96.21% N/A Two eye 99.54% 99.73% High accuracy is possible using both single eye and dual eye cameras Use of second IRIS improves accuracy by 3%

50 FRR Flat Error Curve (DET) 1.00% 0.80% Iris DET (2 Iris, 2 Attempts) Single IRIS Camera Dual IRIS Camera 0.60% 0.46% 0.40% 0.34% 0.33% 0.31% 0.20% 0.27% 0.23% 0.22% 0.21% 0.00% 1.00E E E E-03 FAR Uniquely suitable for high security application

51 FRR Age Distribution Age wise DET Curves (2 Iris, 2 Attempts) 1.20% 1.00% 1.06% 0.80% 0.87% 0.84% 0.81% < % % 0.20% 0.00% 0.29% 0.22% 0.21% 0.20% 0.19% 0.14% 0.14% 0.14% 1.00E E E E-03 FAR >60 Overall accuracy is > 98.94% for all age categories. Children performed best, followed by adults & seniors

52 Observations Two irises authentications provide significant improvement in accuracy and coverage over one iris. Second attempt only marginally improves accuracy. Focus, motion blur or gaze not a major source of false rejects (Matcher 2 seemed to compensate for it)

53 Device Observations Device ergonomics affects Better capture aid for operator and residents can significantly improve image capture Actionable feedback visual aid (LCD on camera, slit for operator for focus) Appropriate visible light source cameras that block ambient light. Improved capture algorithm for special eye conditions KIND 7 image formats

54 Iris Conclusions Over 99.5% population coverage is possible for on-line iris authentication. True accept rate of over 99% is possible Failure : Due to eye surgery (ICCE) (<0.3%) Devices Both single eye and dual-eye work Easy to train, easy to use Further improvement through capture aids Median transaction time < 60 seconds Throughput of 1M/hr is easily achievable

55 Feasibility of FP or Iris authentication Clearly viable in Indian context High (> 98%) coverage and >99% accuracy achievable with 2 fingers or irises. Variety of devices available Iris suitable for children and high security (low FAR) apps. Median Transaction time < 60 secs. 1M/ hour sustained rate easy to achieve Capture can be improved through capture aids.

57 Biometric Challenges Conclusive quality measures at capture point point is everything Fraud detection techniques Reissue/revocation of biometric credential Matching algorithm FP matcher tuning by age group Iris capture matcher for special eye conditions

58 Conclusions Standardize for vendor and technology neutrality Process standards Technology standards (APIs) and certification Multi-vendor, multi-modal approach Use of open source Ecosystem approach to scaling Security and privacy by design Data driven analytics for transparency and continuous improvement 58

59 Thank You