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.

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
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66

67. References (I)
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67

68. References (II)
 Mislove, Alan, et al. "You are who you know: inferring user profiles in
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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