Privacy. Winter School on “Topics in Digital Trust”. IIT Bombay
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Privacy, Anonymization, Fair information practices, privacy decision making, Tor, OECD, FTC
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Privacy. Winter School on “Topics in Digital Trust”. IIT Bombay
- 1. Privacy https://www.linkedin.com/in/ponguru/ Nov 19 - 20, 2022 Winter School on “Topics in Digital Trust” IIT Bombay Ponnurangam Kumaraguru (“PK”) #ProfGiri CS IIIT Hyderabad ACM Distinguished Member TEDx Speaker https://www.instagram.com/pk.profgiri/
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- 5. Long Distance Train Journey 5 https://precog.iiit.ac.in/Publications_files/pk_lc_PET_2005.pdf
- 6. Content around Definition & forms of Privacy Privacy Attitudes & Awareness Social Media Privacy Data anonymity User behavior & Usable privacy Ethics around studying privacy Non Personal Data [Discussion] 6
- 7. 7 https://youtu.be/vAXPzUrqBnI Activity Watch the full video on YouTube [Mandatory]
- 8. Activity 8 https://youtu.be/uaaC57tcci0 Watch the trailer on YouTube [Mandatory] Watch the full documentary on Netflix [Optional]
- 9. Activity 9 https://youtu.be/iX8GxLP1FHo Watch the trailer on YouTube [Mandatory] Watch the full documentary on Netflix [Optional]
- 10. 10 https://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1184&context=fss_papers&httpsredir=1&referer= “Privacy is a value so complex, so entangled in competing and contradictory dimensions, so engorged with various and distinct meanings, that I sometimes despair whether it can be usefully addressed at all.”
- 11. Control over information “Privacy is the claim of individuals, groups or institutions to determine for themselves when, how, and to what extent information about them is communicated to others.” “…each individual is continually engaged in a personal adjustment process in which he balances the desire for privacy with the desire for disclosure and communication….” Alan Westin, Privacy and Freedom, 1967 11
- 12. Westin's 4 states of Privacy • Solitude • individual separated from the group and freed from the observation of other persons • Intimacy • individual is part of a small unit • Anonymity • individual in public but still seeks and finds freedom from identification and surveillance • Reserve • the creation of a psychological barrier against unwanted intrusion - holding back communication 12
- 13. Solove's Privacy Taxonomy • Information Collection • Surveillance • Interrogation • Information Processing • Aggregation • Identification • Secondary Use • Invasion • Intrusion • Decisional Interference 13 https://www.privacysecurityacademy.com/wp-content/uploads/2018/02/Handout-Foundations-and-Themes- Professor-Soloves-Taxonomy-of-Privacy-01.pdf • Information Dissemination • Breach of Confidentiality • Disclosure • Exposure • Increased Accessibility • Blackmail • Appropriation • Distortion
- 15. Westin's Privacy Indexes 15 http://reports-archive.adm.cs.cmu.edu/anon/isri2005/CMU-ISRI-05-138.pdf Fundamentalist – 25% Pragmatist – 60% Unconcerned - 15%
- 16. Privacy in India 16 https://precog.iiit.ac.in/Publications_files/pk_lc_PET_2005.pdf 1st quantitative study in India 2005 study
- 17. India's largest privacy study 2011 / 2012 10,427 respondents 17 https://precog.iiit.ac.in/Publications_files/Niharika-Sachdeva-Privacy-in-India-Demystified-2017.pdf
- 18. 18 What do you feel about privacy of your personal information on your OSN?
- 19. 19 What do you feel about privacy of your personal information on your OSN?
- 20. 20 If you receive a friendship request on your most frequently used OSN, which of the following people will you add as friends? (Choose all that apply)
- 21. 21 If you receive a friendship request on your most frequently used OSN, which of the following people will you add as friends? (Choose all that apply)
- 22. 22 You thought that on the Internet nobody knew you were a dog…
- 23. 23 You thought that on the Internet nobody knew you were a dog… …but then you started getting personalized ads for your favorite brand of dog food
- 24. OECD guidelines Collection limitation Data quality Purpose specification Use limitation Security safeguards Openness Individual participation Accountability 24 https://www.oecd.org/sti/ieconomy/oecd_privacy_framework.pdf
- 25. FTC principles Notice and disclosure Choice and consent Data security Data quality and access Recourse and remedies 25 https://www.ftc.gov/sites/default/files/documents/reports/privacy-online-report-congress/priv-23a.pdf
- 26. 26 Cost of Reading Privacy Policies
- 27. Privacy Enhancing Technologies 27 To balance privacy & utility Minimize personal data use, maximize data security, empower users To express / protect the privacy of entities Protect PII Increase control Choose degree of anonymization Provide informed consent Data minimization
- 28. Privacy Enhancing Technologies: Examples 28 Communication anonymizers – VoIP, P2P networking Shared bogus online accounts – One account created, ID & Pwd posted online, many people use, so one person identity cannot be retrieved Obfuscation – Perturbing the data, adding noise Anonymization – Data can be shared, individuals cant be find in the data Pseudonymization - Ponnurangam Kumaraguru --> CSProf; Reddit Your examples? …
- 33. Privacy Invasive Technologies 33 Does not respect user privacy Spyware – Share user details without user's knowledge RFID – Enable tracking Web bug (web beacons, clear gifs, tracker gifs) Your examples? …
- 34. Privacy decision making To make privacy information more usable to consumers Platform for Privacy Preferences (P3P) XML format that web sites use to encode their privacy policies User software to read P3P policies called a “P3P user agent” 34
- 37. P3P – XML Encoding 37 Lorrie's slides
- 38. Privacy Bird indicator 38 Lorrie's slides
- 39. Privacy Bird indicator 39 Lorrie's slides
- 40. Privacy Bird indicator 40 Lorrie's slides
- 46. 46
- 47. 47
- 48. 48
- 52. Facebook / Congress – Shadow profiles 52 https://youtu.be/JiTQkbLzKUc
- 54. 54
- 55. 55
- 57. 57 https://youtu.be/PArFP7ZJrtg Activity Mandatory Edward Snowden, Permanent Record
- 58. Homework https://docs.google.com/document/d/1iih8B7m0- gIYgFJTLzlxCd4bILKbRkVwW2Gv4GslKTQ/edit 58
- 59. Motivation for anonymization: AOL Search data leak 59 https://en.wikipedia.org/wiki/AOL_search_data_leak
- 60. Motivation for anonymization: AOL Search data leak 60 https://en.wikipedia.org/wiki/AOL_search_data_leak On Aug 4, 2006 650,00 users, 20 Million search keywords, for 3 months Removed on Aug 7, 2006 AOL did not identify the users – PII of users were in the data NY Times re-identified users cross- referencing other sources, including phone book listing AOL ACK the mistake; many copies of the data was distributed “101 Dumbest Moments in business”
- 63. Motivation for anonymization: Netflix Prize 63
- 64. Motivation for anonymization: Netflix Prize 64 Collaborative Filtering algorithm to predict user rating for films based on previous ratings without any other information <user, movie, date of rating, rating> Training: 100M Testing: 2.8M "deleting ratings; inserting alternative ratings and dates; and modifying rating dates” Source code + description to be submitted Jury to decide Started Oct 6, 2006 – June 2007 20,000 entries submitted https://en.wikipedia.org/wiki/Netflix_Prize
- 65. Netflix Prize: Privacy concerns 65
- 68. k-anonymity Suppression – Replace individual attribute with * Generalization – Replace individual attributes with a broader category; Weight: 45 Kgs à Weight: 40 – 50 Kgs 68 First name Last name Age Caste Raj Sharma 25 BC Srishti Rawat 40 GC Manish Sharma 25 BC Srishti Kaur 29 OBC https://www.cs.cmu.edu/~jblocki/Slides/K-Anonymity.pdf
- 69. k-anonymity 69 First name Last name Age Caste * Sharma 25 BC Srishti * * * * Sharma 25 BC Srishti * * *
- 70. k-anonymity 2-anaonymized with suppression 1 & 3, 2 & 4 identical Every cell can be *, but data will be useless Cost of doing is number of *s Fewer cells suppressed to provide k-anonymity 70 First name Last name Age Caste * Sharma 25 BC Srishti * * * * Sharma 25 BC Srishti * * *
- 71. K-anonymity 71
- 73. Limitations Lack of diversity in sensitivity attributes Background knowledge Subsequent release of same dataset 73
- 74. My introduction to Privacy, Anonymity 74
- 75. 75 l-diversity https://slideplayer.com/slide/6027080/ Sensitive attributes must be “diverse” within each quasi-identifier equivalence class
- 76. 76 l-diversity Sensitive attributes must be “diverse” within each quasi-identifier equivalence class
- 78. Limitation Values within one equivalence class may have semantic similarity 78
- 79. 79 t-closeness https://slideplayer.com/slide/6027080/ Distribution of sensitive attributes within each quasi-identifier group should be “close” to their distribution in the entire original database
- 85. Why study Ethics in the Privacy class? 85
- 86. Institutional Review Board (IRB) What is it? Why do we need it? Why study ethics? 86
- 87. Stanford Prison Experiment Randomly assigned participants as guards or prisoners 1971 Arrested prisoners at home 24 UGs in the basement of a building Authoritarian and draconian behaviour Sadistic behaviour by guards Physical abuse Sexual humiliation Sleep on concrete 87
- 89. Milgram Experiment 1961, Yale university Participants to obey Experimenter Shock 65% went to give 450V 89
- 90. Common things Students would do in the project report, presentations Refer the participant(s) by name ”We collected data and my friend Shiva said this” Put screen shot of a conversation on email / chat in presentation without anonymizing / suppressing / blackening the name / email ID Especially if you collect some sensitive information you have to be doubly careful 90
- 91. Institutional Review Board (IRB) IRB Application Consent form Proposal Flyer IRB is only a floor not a ceiling! 91
- 92. What is IRB committee looking for? One being setup in IIITH now Is this the only way to collect the information that Researcher is interested in? What protection is giving to the information that is collected? Is there any implications of the data beyond the study? 92
- 93. 93
- 94. Reactions for the study Anger Unethical, inappropriate, illegal, fraudulent Researchers fired Psychological cost Denial Nobody accepted that they fell for it Admitting our vulnerability is hard Misunderstanding over spoofing emails Underestimation of publicly available information 94
- 95. Research Failures U.S. Office for Human Research Protection suspended all research at John Hopkins University after one research participant died May 1999, federal regulators temporarily stopped research at Duke over concerns of volunteer safety University of Pennsylvania, an 18-year-old died in Sept 1999 from drugs administered as part of a gene therapy study Other studies where participants have died 95
- 96. Principles of Research with Human Subjects Respect for Persons individuals have autonomy and choice people cannot be used as a means to an end provide protection to the vulnerable provide informed consent and privacy 96
- 97. Principles of Research with Human Subjects Beneficence kindness beyond duty obligation to do no harm obligation to prevent harm obligation to do good minimize risks, maximize benefits 97
- 98. Principles of Research with Human Subjects Justice treat all fairly share equitably burdens and benefits 98
- 99. Federal regulations, implementation Respect for Persons -- Informed Consent Beneficence -- Assessment of risk and benefits, minimize risk Justice -- Fair selection of participants 99
- 102. Our responsibility Researchers have a responsibility to know how to conduct research ethically 102
- 105. Takeaways No dearth of problems to study… You should be keen to look for it around… Will be happy to discuss anything further… 105
- 106. Acknowledgements Lorrie Cranor’s slides Thanks to faculty who taught courses on these topics 106