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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 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 first 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
Citation preview
OCEAN: Open-source Collation of eGovernment data And NetworksUnderstanding Privacy Leaks in Open
Government Data
Srishti Gupta
Advisor: Dr. Ponnurangam Kumaraguru
M.Tech Thesis Defense
20-November-2013
Thesis Committee
Dr. Muttukrishnan Rajarajan, City University, London
Dr. Vinayak Naik, IIIT-Delhi
Dr. PK (Chair), IIIT-Delhi
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Demo
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Academic Honors
Gupta, S., Gupta, M., and Kumaraguru, P. OCEAN: Open-source Collation of eGovernment data And Networks. Poster at Security and Privacy Symposium (SPS), IIT-K, 2013.
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.
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BEST Poster
Recognition
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IIITD Homepage [ Aug ’13 ]
Hindustan [ April ’13 ]
550 Unique Visitors
(as on Nov 17, 2013)
Presentation Outline
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Presentation Outline
Research Motivation and Aim
Related Work
Research Contribution
Methodology
Experiments and Analysis
Conclusion
Future Work
Questions
Identity Theft- On rise!
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Research Motivation and Aim
Ways to get PII
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Mail Thefts, Pharming
OSN
Social Engineering (e.g., Fake accounts)
Not credible Limited Info.
Open Government Data Source
E-mail, Docs, Spreadsheet
Shoulder SurfingDumpster Diving
Open Government Data Sources ‘Open’: Publicly available
eGovernment initiatives by different state government inthe 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 informationdisclosure, privacy breach.
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Research Motivation and Aim
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Information Leakage in Open Government Data Sources ??
PII Leakage
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Personally Identifiable Information (PII)
Voter ID, Name, Father’s name, Age, Gender, Date Of Birth, DL number, PAN, Phone number
Research Motivation and Aim
The Other Side! “People’s View”
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Research Motivation and Aim
CONSCIOUS DECISION !
(Kumaraguru, 2012)
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Citizens do not want their PII to be leaked !
Research Aim
To develop a technology to showcase publicly availablepersonal information online
To highlight the privacy issues on aggregation of availablepersonal information
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Research Motivation and Aim
System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data ExtractionEvaluation (Privacy Score, Recall, SUS)
Presentation Outline
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Presentation Outline
Research Motivation and Aim
Related Work
Research Contribution
Methodology
Experiments and Analysis
Conclusion
Future Work
Questions
Related Work
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Related Work and Research Contribution
Yasni(www.yasni.com)
Related Work
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Related Work and Research Contribution
Pipl(www.pipl.com)
Related Work
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Related Work and Research Contribution
Name Country Description
IndianKanoon(http://www.indiankanoon.org/)
India Legal search engine Indexes judgements of the Supreme Court and several High
Courts
OpenCivic.in(http://www.opencivic.in/)
India Application Programming Interface Gives data about state assembly elections and profiles of MP's in
Maharashtra
ABQ Ride(http://www.cabq.gov/abq-apps/city-apps-listing/abq-ride)
USA Real-time locations of city buses Fares for other public transportation
Illustreets(http://data.gov.uk/apps/illustreets)
UK Comparing locations Gives crime, education, transport and census data for a location
Various country-specific systems built with Open Government Data
Research Gap
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OCEANYasni / Pipl
Open Source Data Aggregation
Indian KanoonOpen Government Data
PII Leakage
Related Work and Research Contribution
Presentation Outline
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Presentation Outline
Research Motivation and Aim
Related Work
Research Contribution
Methodology
Experiments and Analysis
Conclusion
Future Work
Questions
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 andexpectations of the users from the open government data.
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Related Work and Research Contribution
Presentation Outline
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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
System Architecture
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System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data Extraction Evaluation (Privacy Score, Recall, SUS)
Driving LicenceDL-XXYYYYAAAAAAA where
DL: state(Delhi), XX: Location in Delhi, YYYY: Year of issue of the license, AAAAAAA is unique
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Methodology
Voter ID XXX12345678 where
X: ‘A’ – ‘Z’ and last 8 digits- numerals
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Methodology
PAN XXXTL1234X where
XXX: ‘A’ – ‘Z’, T: Type of holder, L: First character of last-name,1234: Sequential number, X: Check digit
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Methodology
Online Social Networks
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Methodology
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
System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data ExtractionEvaluation (Privacy Score, Recall, SUS)
II. Threat Modelling
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Methodology
USER
OPEN GOVERNMENT
DATA
PAN
DRIVING LICENSE VOTER ROLLS
Name, Address, Father’s name, Driving License no., DOB
Driving License number
Name, Constituency
Name, Address, Relation name, Age, Gender, Voter ID
Name, PANName, DOB
TRUST BOUNDARY
Attack Scenario (I) Online Voter ID card – Multiple fake voter ID cards can be
created from the available PII
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Research Motivation and Aim
Attack Scenario (II) View tax statements (Income tax e-filing) – Fake accounts
can be created to view TDS statements.
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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
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Research Motivation and Aim
II. Threat Modelling DREAD Model: Microsoft’s Risk Assessment Model
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Methodology
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 toexploit 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?
II. Threat ModellingScheme: High (3), Medium (2), Low (1)
Threat: Malicious user can identify PII of Delhi residents
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Methodology
[Threat modelling: http://msdn.microsoft.com/en-us/library/ff648644.aspx]
II. Threat ModellingAccording to Microsoft’s DREAD model,
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 !
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Methodology
Range Level of risk
5 -7 Low
8 – 11 Medium
12 – 15 High
System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data ExtractionEvaluation (Privacy Score, Recall, SUS)
III. Data ExtractionData was collected from various open government data sources usingPHP scripts and stored as MySQL databases.
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Methodology
OPEN GOVT. WEBSITES
Alphabets a-z for name, across 70 constituencies
Name and DOB from DL
Random 5 seeds, ‘Incremental attack’
PAN
[53,419]
DRIVING LICENCE[2,24,982]
VOTER[81,95,053]
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.
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Methodology
UNIQUE NAME
API CALLS
GOOGLEPLUS[28,900]
LINKEDIN[1,86,798]
FOURSQUARE[29,393]
TWITTER[15,57,715]
FACEBOOK[33,77,102]
System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data ExtractionEvaluation (Privacy Score, Recall, SUS)
IV. Information Aggregation Family Tree
Information within Voter ID database aggregated to findrelationships among records.
OCEAN has 3,90,353 such users.
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Methodology
IV. Information Aggregation Mapping of users across Voter ID and Driving licence database.
Table Schema:
Done on the basis of similarity between name, relation name andaddress of the users across the database.
OCEAN has 6,384 such users.
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Methodology
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
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IV. Information AggregationMethodology
Challenge: The address formats for various sources is different
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.
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Methodology
IV. Information Aggregation Mapping users across Foursquare, Facebook and Twitter.
Some users specify their other OSN’s contact on Foursquare. Theinformation available from such users is aggregated together.
OCEAN has 11 such users
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Methodology
IV. Information Aggregation
Challenge: Not many users link their OSN accounts
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Methodology
Presentation Outline
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Presentation Outline
Research Motivation and Aim
Related Work and Research Contribution
Methodology
System User Interface
Experiments and Analysis
Conclusion
Future Work
Questions
System Outline
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Presentation Outline
Identification of data sources
Threat Modelling Information Aggregation
Data ExtractionEvaluation (Privacy Score, Recall, SUS)
Survey Dataset
62 complete responses.
51% males, 49% females.
77% in the age group 20 – 25.
23% had friends / self experience identity thefts online.
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Experiments and Analysis
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Experiments and Analysis
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
Evaluation Metric I - Privacy Score
Privacy Score
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Experiments and Analysis
Level 5 1Willingness to share
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
Privacy Score
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Experiments and Analysis
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
Privacy Score
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Experiments and Analysis
1,693 people
Highest Risk!
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
Evaluation Metric II
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Evaluation Metrics
Recall (Based on user study)
𝑅𝑒𝑐𝑎𝑙𝑙 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑤ℎ𝑜 𝑐𝑜𝑢𝑙𝑑 𝑏𝑒 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝑠𝑦𝑠𝑡𝑒𝑚
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.
Evaluation Metric III
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Evaluation Metrics
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)
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.
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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.
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Experiments and Analysis
User Expectations
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Experiments and Analysis
Feedback
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Feedback
“It was an eye-openerto a common man.”
I am really shocked that the exact ID
numbers are available online without much security against data mining at this scale.”
“Waiting for an upgraded version
which will work for other states also.”
“Good system. Great work ! Didn't know
such a system existed.”
“A great shortcoming and security flaw has been pointed out by OCEAN. Great work.”
Presentation Outline
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Presentation Outline
Research Motivation and Aim
Related Work
Research Contribution
Methodology
Experiments and Analysis
Conclusion
Future Work
Questions
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.
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Conclusion
Presentation Outline
63
Presentation Outline
Research Motivation and Aim
Related Work
Research Contribution
Methodology
Experiments and Analysis
Conclusion
Future Work
Questions
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.
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Future Work
Acknowledgments
Mayank Gupta, B.Tech, DCE
Niharika Sachdeva, PhD, IIIT-Delhi
Precog members, friends and family
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Future Work
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Thank You!
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Questions?