Big Data for a Better World
- 1. Big Data For A Better World Sponsors:
- 2. Tonight’s schedule • Panel Presentation • Keynote • Networking Big Data For A Better World
- 3. Panel Presentation • Leon Wilson • David M. Holmes • Jason Therrien Big Data For A Better World
- 4. Big Data: The Promise, the Premise and the Practice Kambiz (come-bees) Ghazinour Advanced Information Security and Privacy Research Lab Kent State University Nov 16, 2017
- 5. Big Data: The Promise, the Premise and the Practice
- 6. Big Data: The Promise, the Premise and the Practice Volume Velocity Variety Veracity
- 7. Big, Fast, Diverse, Uncertain Data: The Promise, the Premise and the Practice Google, MapReduce 2004
- 8. Big, Fast, Diverse, Inaccurate Data: The Problem of Promising Protection of Personal Information in a Protected and Privacy Preserving Platform in Practice
- 9. Phone Metadata The Stanford Experiment: See Data and Goliath by Bruce Schneider • phone metadata from 500 volunteers • One called a hospital, a medical lab, a pharmacy, and several short calls to a monitoring hotline for a heart monitor • One called her sister at length, then calls to an abortion clinic, further calls two weeks later, and a final call a month later. • a heart patient, an abortion … • This is just metadata not content. It’s very revealing. Who should be able to see it?
- 10. Personal Data and Privacy No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his honor and reputation. Everyone has the right to the protection of the law against such interference or attacks • Article12 of the Universal Declaration of Human Rights
- 11. Nothing to hide, nothing to fear? • Many people need to control their privacy – victims of rape or other trauma, – people escaping abusive relationships – people who may be discriminated against (HIV positive, previous mental illness, spent convictions, recovering addicts) – people at risk of “honor” violence from their families for breaking cultural norms – Adopters, protecting their adopted children from the birth families that abused them – witness protection, undercover police, some social workers and prison staff …. • It is unthinking or callous to see other people’s privacy as unimportant. • Data is forever and your circumstances or society’s attitudes may change
- 12. The Value of Big Data • Facebook and Amazon are valued at $500B, as of July 2017 • Most of Facebook’s value comes from personal data
- 13. Anonymization • Statistical analyses are anonymous “70 percent of American smokers want to quit” does not expose personal data • Data about individuals can be anonymous, but it becomes very difficult when more than a few facts are included even if these facts are not specific and some of them are wrong (eg Netflix)
- 14. 3 ways to anonymize • Suppress - omit from the released data • Generalize - for example, replace birth date with something less specific, like birth year • Perturb - make changes to the data
- 16. Example 1: AOL Search Data August 2006 • To stimulate research into the value of search data, AOL released the anonymized search records of 658,000 users over a three month period from March to May 2006
- 17. AOL anonymization • AOL had tried to anonymize the data they released by removing the searcher’s IP address and replacing the AOL username with a unique random identifier linking of the searches by any individual, so that the data was still useful for research • It did not take long for two journalists to identify user 4417749, who had searched for people with the last name Arnold, “homes sold in shadow lake subdivision Gwinnett county Georgia" and “pine straw in Lilburn” as Thelma Arnold, a widow living in Lilburn, Georgia • Her other searches provide a deeply personal view of her life, difficulties and desires
- 18. AOL faced strong criticism • The violation of privacy was widely condemned • AOL described their action as a “screw up” • They took down the data, but it was too late. The internet never forgets. Several mirror sites had already been set up.
- 21. Example 2: The Netflix™ Prize • In October 2006, Netflix launched a $1m prize for an algorithm that was 10% better than its existing algorithm Cinematch • participants were given access to the contest training data set of more than 100 million ratings from over 480 thousand randomly-chosen, anonymous customers on nearly 18 thousand movie titles. • How much information would you need to be able to identify customers?
- 22. Netflix • Netflix said “to protect customer privacy, all personal information identifying individual customers has been removed and all customer ids have been replaced by randomly-assigned ids. The date of each rating and the title and year of release for each movie are provided. No other customer or movie information is provided.” • Two weeks after the prize was launched, Arvind Narayanan and Vitaly Shmatikov of the University of Texas at Austin announced that they could identify a high proportion of the 480,000 subscribers in the training data.
- 23. Narayanan and Shmatikov’s results • How much does the attacker need to know about a Netflix subscriber in order to identify her record in the dataset, and thus completely learn her movie viewing history? Very little. • For example, suppose the attacker learns a few random ratings and the corresponding dates for some subscriber, perhaps from coffee-time chat. • With 8 movie ratings (of which we allow 2 to be completely wrong) and dates that may have a 3-day error, 96% of Netflix subscribers whose records have been released can be uniquely identified in the dataset. • For 64% of subscribers, knowledge of only 2 ratings and dates is sufficient for complete deanonymization, and for 89%, 2 ratings and dates are enough to reduce the set of records to 8 out of almost 500,000, which can then be inspected for further deanonymisation.
- 24. Why are Narayanan and Shmatikov’s results important? 1. They were results from probability theory, so they apply to all sparse datasets. (They tested the results later, using the Internet Movie Database IMDb as a source of data). 2. Psychologists at Cambridge University have shown that a small number of seemingly innocuous Facebook Likes can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender).
- 28. The Attack Scenario - Anonymization 28
- 30. Re-identification by linking (example)
- 31. Anonymization in Data Systems 31
- 32. K-anonymity
- 33. K-Anonymity
- 35. Example of suppression and generalization
- 38. Example
- 39. Finding similar instances • A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space – Locality sensitive hashing (LSH) – Error Weighted Hashing (EWH) – Etc. 39
- 40. Big, Fast, Diverse, Inaccurate Data: The Problem of Promising Protection of Personal Information in a Protected and Privacy Preserving Platform in Practice
- 41. Thank you! • Questions? Kambiz Ghazinour kghazino@kent.edu @DrGhazinour 41 Advanced Information Security and Privacy Lab
- 42. Big Data For A Better World • Networking Sponsors: