OCEAN: Open-source Collation of eGovernment data And Networks: Understanding Privacy Leaks in Open Government Data

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The awareness and sense of privacy has increased in the minds of people over the past few years. Earlier, people were not very restrictive in sharing their personal information, but now they are more …

The awareness and sense of privacy has increased in the minds of people over the past few years. Earlier, people were not very restrictive in sharing their personal information, but now they are more cautious in sharing it with strangers, either in person or online. With such privacy
expectations and attitude of people, it is difficult to embrace the fact that a lot of information is publicly available on the web. Information portals in the form of the e-governance websites run by Delhi Government in India provide access to such PII without any anonymization. Several databases e.g., Voterrolls, Driving Licence number, MTNL phone directory, PAN card serve as
repositories of personal information of Delhi residents. This large amount of available personal information can be exploited due to the absence of proper written law on privacy in India. PII can also be collected from various social networking sites like Facebook, Twitter, GooglePlus etc. where the users share some information about them. Since users themselves put this information, it may not be considered as a privacy breach, but if the information is aggregated, it may give out much more information resulting in a bigger threat. For e.g., data from social networks and open government databases can be combined together to connect an online identity to a real world identity. Even though the awareness about privacy has increased, the threats possible due to the
availability of this large amount of personal data is still unknown. To bring such issues to public notice, we developed Open-source Collation of eGovernment data And Networks (OCEAN), a system where the user enters little information (e.g. Name) about a person and gets large
amount of personal information about him / her like name, age, address, date of birth, mother's name, father's name, voter ID, driving licence number, PAN. On aggregation of information within the Voter ID database, OCEAN creates a family tree of the user giving out the details of his / her family members as well. We also calculated a privacy score, which calculates the risk associated with that individual in terms of how much PII of that person is revealed from open government data sources. 1,693 users had the highest privacy score making them the most
vulnerable to risks. Using OCEAN, we could collect 8,195,053 Voterrolls; 2,24,982 Driving licence; 53,419 PAN card numbers; 1,557,715 Twitter; 3,377,102 Facebook; 29,393 Foursquare; 1,86,798 LinkedIn and 28,900 GooglePlus records. We received 661 total hits (657 unique visitors) from the day we released the system, January 21, 2013, until October 10, 2013. To the best of our knowledge, this is the fi rst real world deployed tool which provides personal information about residents of Delhi to everyone free of cost.

Full Report: http://arxiv.org/abs/1312.2784

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  • 1. OCEAN: Open-source Collation of eGovernment data And Networks Understanding Privacy Leaks in Open Government Data Srishti Gupta Advisor: Dr. Ponnurangam Kumaraguru M.Tech Thesis Defense 20-November-2013
  • 2. Thesis Committee  Dr. Muttukrishnan Rajarajan, City University, London  Dr. Vinayak Naik, IIIT-Delhi  Dr. PK (Chair), IIIT-Delhi 2
  • 3. Demo 3
  • 4. Academic Honors  Gupta, S., Gupta, M., and Kumaraguru, P. OCEAN: Open- Poster source Collation of eGovernment data And Networks. Poster at Security and Privacy Symposium (SPS), IIT-K, 2013. BEST  Gupta, S., Gupta, M., and Kumaraguru, P. Is Government a Friend or Foe? Privacy in Open Government Data. Poster at IBM-ICARE, IISc Bangalore, 2012. 4
  • 5. Recognition IIITD Homepage [ Aug ’13 ] Hindustan [ April ’13 ] 550 Unique Visitors (as on Nov 17, 2013) 5
  • 6. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology  Experiments and Analysis  Conclusion  Future Work  Questions 6
  • 7. Research Motivation and Aim Identity Theft- On rise! 7
  • 8. Ways to get PII OSN E-mail, Docs, Spreadsheet Mail Thefts, Pharming Shoulder Surfing Dumpster Diving Social Engineering (e.g., Fake accounts)  Not credible  Limited Info. Open Government Data Source 8
  • 9. Research Motivation and Aim Open Government Data Sources  ‘Open’: Publicly available  eGovernment initiatives by different state government in the form of databases / services.  Objective?  Improve information gathering procedure  Reduce the burden on citizens to access their data  Pros: Improved data availability, easy verification.  Cons: Databases publicly available, leading to information disclosure, privacy breach. 9
  • 10. Information Leakage in Open Government Data Sources ?? 10
  • 11. Research Motivation and Aim PII Leakage Voter ID, Name, Father’s name, Age, Gender, Date Of Birth, DL number, PAN, Phone number Personally Identifiable Information (PII) 11
  • 12. Research Motivation and Aim The Other Side! “People’s View” CONSCIOUS DECISION ! (Kumaraguru, 2012) 12
  • 13. Citizens do not want their PII to be leaked ! 13
  • 14. Research Motivation and Aim Research Aim  To develop a technology to showcase publicly available personal information online  To highlight the privacy issues on aggregation of available personal information 14
  • 15. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 15
  • 16. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology  Experiments and Analysis  Conclusion  Future Work  Questions 16
  • 17. Related Work and Research Contribution Related Work Yasni (www.yasni.com) 17
  • 18. Related Work and Research Contribution Related Work Pipl (www.pipl.com) 18
  • 19. Related Work and Research Contribution Related Work Various country-specific systems built with Open Government Data Name Country Description IndianKanoon India  Legal search engine  Indexes judgements of the Supreme Court and several High Courts India  Application Programming Interface  Gives data about state assembly elections and profiles of MP's in Maharashtra USA  Real-time locations of city buses  Fares for other public transportation UK  Comparing locations  Gives crime, education, transport and census data for a location (http://www.indiankano on.org/) OpenCivic.in (http://www.opencivic.i n/) ABQ Ride (http://www.cabq.gov/a bq-apps/city-appslisting/abq-ride) Illustreets (http://data.gov.uk/app s/illustreets) 19
  • 20. Related Work and Research Contribution Research Gap Indian Kanoon Open Government Data Open Source Data Aggregation OCEAN Yasni / Pipl PII Leakage 20
  • 21. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology  Experiments and Analysis  Conclusion  Future Work  Questions 21
  • 22. Related Work and Research Contribution Research Contribution  First deployed system which shows the aggregated personal information about the residents of Delhi.  Threat modelling on the various open government databases.  Privacy Score: Risk associated with the person on the leaking PII.  Empirical understanding of privacy perceptions, awareness and expectations of the users from the open government data. 22
  • 23. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology     Identification of open government data sources Threat Modelling Data Extraction Information Aggregation  Experiments and Analysis  Conclusion  Future Work  Questions 23
  • 24. System Architecture 24
  • 25. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 25
  • 26. Methodology Driving Licence DL-XXYYYYAAAAAAA where DL: state(Delhi), XX: Location in Delhi, YYYY: Year of issue of the license, AAAAAAA is unique 26
  • 27. Methodology Voter ID XXX12345678 where X: ‘A’ – ‘Z’ and last 8 digits- numerals 27
  • 28. Methodology PAN XXXTL1234X where XXX: ‘A’ – ‘Z’, T: Type of holder, L: First character of last-name, 1234: Sequential number, X: Check digit 28
  • 29. Methodology Online Social Networks Name , Gender, Profile image, Profile url Name , Followers / Following count, Location, Profile image, Profile url Name , Gender, Facebook / Twitter contact, Friend / Follower count, Badge / Mayorship / Check-in count, Location, Profile image, Profile url Name , Location, Profile image, Profile url Name , Gender, Relationship status, Location, Organization, Birthday, E-mail, Language, Profile image, Profile url 29
  • 30. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 30
  • 31. Methodology II. Threat Modelling TRUST BOUNDARY USER Name, Address, Relation name, Age, Gender, Voter ID Driving License number DRIVING LICENSE Name, Address, Father’s name, Driving License no., DOB OPEN GOVERNMENT DATA Name, DOB VOTER ROLLS Name, Constituency Name, PAN PAN 31
  • 32. Research Motivation and Aim Attack Scenario (I)  Online Voter ID card – Multiple fake voter ID cards can be created from the available PII 32
  • 33. Research Motivation and Aim Attack Scenario (II)  View tax statements (Income tax e-filing) – Fake accounts can be created to view TDS statements. 33
  • 34. Research Motivation and Aim Attack Scenario (III)  Procure a SIM card / phone connection  Fake documents can be created  Credit / debit cards can be applied in victim’s name  Networking accounts can be created 34
  • 35. Methodology II. Threat Modelling DREAD Model: Microsoft’s Risk Assessment Model Term Remarks Damage How big the damage would be if the attack succeeded? Reproducibility How easy it is to reproduce the attack to work? Exploitability How much time, effort, and expertise is needed to exploit the threat? Affected Users If a threat were exploited, what percentage of users would be affected? Discoverability How easy is it for an attacker to discover this threat? 35
  • 36. Methodology II. Threat Modelling Scheme: High (3), Medium (2), Low (1) Threat: Malicious user can identify PII of Delhi residents [Threat modelling: http://msdn.microsoft.com/en-us/library/ff648644.aspx] 36
  • 37. Methodology II. Threat Modelling According to Microsoft’s DREAD model, Range Level of risk 5 -7 Low 8 – 11 Medium 12 – 15 High In our case, Overall rating = 2 + 3 + 2 + 3 + 3 = 13 (High) It means that this threat pose a significant risk to the various information portal websites of Delhi government and needs to be addressed as soon as possible ! 37
  • 38. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 38
  • 39. Methodology III. Data Extraction Data was collected from various open government data sources using PHP scripts and stored as MySQL databases. OPEN GOVT. WEBSITES Alphabets a-z for name, across 70 constituencies Random 5 seeds, ‘Incremental attack’ Name and DOB from DL VOTER [81,95,053] DRIVING LICENCE [2,24,982] PAN [53,419] 39
  • 40. Methodology III. Data Extraction  Public data from various online social networking sites was collected using public API calls.  OAuth tokens were used for authentication and authorization. FACEBOOK [33,77,102] TWITTER [15,57,715] FOURSQUARE [29,393] UNIQUE NAME GOOGLEPLUS [28,900] API CALLS LINKEDIN [1,86,798] 40
  • 41. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 41
  • 42. Methodology IV. Information Aggregation  Family Tree  Information within Voter ID database aggregated to find relationships among records.  OCEAN has 3,90,353 such users. 42
  • 43. Methodology IV. Information Aggregation  Mapping of users across Voter ID and Driving licence database. Table Schema: Database Attributes Voter ID Voter ID, Name, Address, Father's / Mother's / Husband's name, Age, Gender Driving Licence Name, Address, Father's name, DOB, Validity period, vehicle category  Done on the basis of similarity between name, relation name and address of the users across the database.  OCEAN has 6,384 such users. 43
  • 44. Methodology IV. Information Aggregation Challenge: The address formats for various sources is different 44
  • 45. Methodology IV. Information Aggregation  Mapping of users across Voter ID, Driving licence and PAN database.  Subset of DL having PAN were chosen.  OCEAN has 1,693 such users. 45
  • 46. Methodology IV. Information Aggregation  Mapping users across Foursquare, Facebook and Twitter.  Some users specify their other OSN’s contact on Foursquare. The information available from such users is aggregated together.  OCEAN has 11 such users 46
  • 47. Methodology IV. Information Aggregation Challenge: Not many users link their OSN accounts 47
  • 48. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work and Research Contribution  Methodology  System User Interface  Experiments and Analysis  Conclusion  Future Work  Questions 48
  • 49. Presentation Outline System Outline Identification of data sources Data Extraction Threat Modelling Evaluation (Privacy Score, Recall, SUS) Information Aggregation 49
  • 50. Experiments and Analysis Survey Dataset  62 complete responses.  51% males, 49% females.  77% in the age group 20 – 25.  23% had friends / self experience identity thefts online. 50
  • 51. Experiments and Analysis Evaluation Metric I - Privacy Score  Privacy score measure the risk associated with a person on the basis of how much PII about that person is revealed from open government data sources.  Privacy score (user) = Σ Sensitivity score (attributes)  Sensitivity score -> {1, 2, 3, 4, 5} Range Level <20 % 1 21 – 30 % 2 31 – 50 % 3 51 – 60 % 4 >61 % 5 51
  • 52. Experiments and Analysis Privacy Score Attribute Percentage of users unwilling to share personal information with anyone Privacy Level Voter ID 56.4% 4 Driving licence no. 58% 4 PAN 67.7% 5 Full name 14.5% 1 Home address 82.25% 5 Age 29% 2 DOB 50% 3 Father’s name 38.7% 3 Gender 14.5% 1 Level 5 1 Willingness to share 52
  • 53. Experiments and Analysis Privacy Score Privacy score for 84,22,459 users:  Case 1: Users having only Voter ID (97.3%) PS = Σ(Voter ID, name, father’s name, age, gender, address) = 16  Case 2: Users having only Driving licence number (2%) PS = Σ(DL number, name, relative’s name, DOB, address) = 17  Case 3: Users having only PAN (1%) PS = Σ(PAN, DL number, name, relative’s name, DOB, address) = 25 53
  • 54. Experiments and Analysis Privacy Score  Case 4: Users having Voter ID and DL number (0.07%) PS = Σ(Voter ID, DL number, name, father’s name, age, gender, DOB, address) = 24  Case 5: Users having Voter ID, DL number and PAN (0.02%) PS = Σ(Voter ID, DL number, PAN, name, father’s name, age, gender, DOB, address) = 29 1,693 people Highest Risk! 54
  • 55. Evaluation Metrics Evaluation Metric II  Recall (Based on user study) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑤ℎ𝑜 𝑐𝑜𝑢𝑙𝑑 𝑏𝑒 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑦𝑠𝑡𝑒𝑚 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑒𝑎𝑟𝑐ℎ 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑑𝑜𝑛𝑒 𝑜𝑛 𝑡ℎ𝑒 𝑠𝑦𝑠𝑡𝑒𝑚 Thus, Recall = ( 179 / 389 ) = 46% Low Recall  Data collection not 100%. (Out of 12 million voter records, we have ~8 million records)  Respondents might be unclear about constituency. 55
  • 56. Evaluation Metrics Evaluation Metric III  System Usability Score (SUS) Measured using the standard method as defined by Brooke et.al. For OCEAN, value was 74.5 / 100 which means that people found the system usable and convenient to use. (Brooke, 1996) 56
  • 57. Experiments and Analysis User Awareness  Government started various open initiatives to increase the level of transparency with citizens.  But, only 19% survey respondents aware.  Around 76% have started using these for less than 2 years.  Proper schemes required to convey the existence. 57
  • 58. Experiments and Analysis User Experience  Majority, 62% were shocked to see the availability of personal information to this extent.  People felt that the information can be used maliciously against them.  People now feel scared in sharing their information with various government departments. 58
  • 59. Experiments and Analysis User Expectations 59
  • 60. Feedback Feedback “It was an eye-opener to a common man.” “Waiting for an upgraded version which will work for other states also.” I am really shocked that the exact ID numbers are available online without much security against data mining at this scale.” “A great shortcoming and security flaw has been pointed out by OCEAN. Great work.” “Good system. Great work ! Didn't know such a system existed.” 60
  • 61. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology  Experiments and Analysis  Conclusion  Future Work  Questions 61
  • 62. Conclusion Conclusion  Large amount of personal information is available on government servers.  Information aggregation yields more information about a person.  Threat Modelling on open government data sources shows risk associated with PII leakage and need for preventive measures.  1,693 users are most vulnerable to identity thefts risks.  People felt the need of access control on the data and proper privacy laws against the misuse of information. 62
  • 63. Presentation Outline Presentation Outline  Research Motivation and Aim  Related Work  Research Contribution  Methodology  Experiments and Analysis  Conclusion  Future Work  Questions 63
  • 64. Future Work Future Work  Datasets can be extended to other states in India.  Mapping users across offline (govt. databases) and online (social networking sites) worlds.  Data collection can be expanded to improve the recall. 64
  • 65. Future Work Acknowledgments Mayank Gupta, B.Tech, DCE Niharika Sachdeva, PhD, IIIT-Delhi Precog members, friends and family 65
  • 66. References  Kumaraguru, P., and Sachdeva, N. Privacy in India: Attitudes and Awareness V 2.0. Tech. rep., PreCog-TR-12-001, PreCog@IIIT-Delhi, 2012. http://precog.iiitd.edu.in/research/privacyindia/  McCallister, Erika, Tim Grance, and Karen Scanfone. "Guide to protecting the confidentiality of personally identifiable information (PII)(draft), January 2009." NIST Special Publication: 800-122.  Schwartz, Paul M., and Daniel J. Solove. "PII Problem: Privacy and a New Concept of Personally Identifiable Information, The." NYUL Rev. 86 (2011): 1814.  Mont, Marco Casassa, Siani Pearson, and Pete Bramhall. "Towards accountable management of identity and privacy: Sticky policies and enforceable tracing services." Database and Expert Systems Applications, 2003. Proceedings. 14th International Workshop on. IEEE, 2003.  Jones, Rosie, et al. "I know what you did last summer: query logs and user privacy." Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. ACM, 2007 66
  • 67. References (I)  Nashash, Hyam. "EDUCATION AS A BUILDING BLOCK IN OPENING UP GOVERNMENT DATA." European Scientific Journal 9.13 (2013).  Barber, Grayson. "Personal Information in Government Records: Protecting the Public Interest in Privacy." . Louis U. Pub. L. Rev. 25 (2006): 63.  Krishnamurthy, Balachander, and Craig E. Wills. "On the leakage of personally identifiable information via online social networks." Proceedings of the 2nd ACM workshop on Online social networks. ACM, 2009.  Jurgens, David. "That’s What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships." Seventh International AAAI Conference on Weblogs and Social Media. 2013.  Zheleva, Elena, and Lise Getoor. "To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles." Proceedings of the 18th international conference on World wide web. ACM, 2009. 67
  • 68. References (II)  Mislove, Alan, et al. "You are who you know: inferring user profiles in online social networks." Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010.  Harel, Amir, et al. "M-score: estimating the potential damage of data leakage incident by assigning misuseability weight." Proceedings of the 2010 ACM workshop on Insider threats. ACM, 2010.  Wright, Glover, Pranesh Prakash Sunil Abraham, and Nishant Shah. "Open government data study: India." Study commissioned by the Transparency and Accountability Initiative (2010).  Godse, Mr Vinayak, and Director–Data Protection. "RISE PROJECT." (2010).bibitem{brooke1996sus} Brooke, John. ``SUS-A quick and dirty usability scale." Usability evaluation in industry 189 (1996): 194.  Social media report 2012: Social media comes of age. http://www.nielsen.com/us/en/reports/2012/state-of-the-media-thesocial-media-report-2012.html 68
  • 69. Thank You! 69
  • 70. Questions? 70
  • 71. For any further information, please write to pk@iiitd.ac.in precog.iiitd.edu.in 71