29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Data Mining Dr. Bradley A. Malin Assistant Professor Department of Biomedical Informatics Vanderbilt University

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Data Collection Localized: Personal records in databases at source Distributed: Integration of records from many sources Overt Collection Hospital visit for treatment Covert Collection webcam while walking Electronic Medical Records demographics clinical presentation

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Data Mining Unsupervised –Labels unknown in advance, so search for intrinsic patterns of the data –Clustering similar people Purchased same products Supervised –Labels known in advance –Train models on sample data to classify new cases Country Age USACanada Age <50 >50 Jaws Harry Potter 1984 Scream

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Website Personalization Can a website predict what I want to see? –Intra-personalization: What pages / topics did I visit in my previous visits? –Inter-personalization: Is my browsing / purchasing history similar to other peoples? Does my behavior reveal my identity or sensitive things about my life? –What information should not be revealed?

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Intelligence Lists of entities are becoming increasingly prevalent –Intelligence reports, rosters, networks How many Alices are there? Which is which? How does Alice relate to Bob? Alice Doe Bob Doe Junior Doe doc A Alice Doe John Smith doc C Bob Doe Junior Doe Alice Doe doc B Alice Doe John Smith Brad Malin doc D

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Location Surveillance: Did someone on Interpols watchlist visit hotel X? Airline Y? Challenge: Data holders want to collaborate, but fear strategic knowledge and legal constraints Surveillance Hotel XAirline Y

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Privacy Protections Protect Anonymity –Remove / encrypt identifying information –Suppress inferences that can reveal identity Protect Confidentiality –Hide Sensitive Rules –Perturbation and Generalization Secure Multiparty Computation –E(a) + E(b) = E(a + b) [homomorphism] –E(E(John),x),y) = E(E(John),y),x) [commutate]

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Clinical Genomics Vanderbilt DNA Databank DNA from leftover blood –25-75K per year, 250K in 5 years Combined with de-identified electronic medical records –600 GBytes on 1.4 mil. patients Hypothesis Generation to mine correlations between clinical features and DNA Blood Samples Clinical Record Clinical Record DNA 512 Bit Hash of # De-identification Linked by Medical Record #

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Example De-identified Medical Record Substituted names Replaced SSN and phone # Shifted Dates MR# is removed

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive Naïve Protection Patterns in data can lead to privacy compromise Suppress patterns intelligently to support goals

29e CONFÉRENCE INTERNATIONALE DES COMMISSAIRES À LA PROTECTION DES DONNÉES ET DE LA VIE PRIVÉE 29 th INTERNATIONAL CONFERENCE OF DATA PROTECTION AND PRIVACY COMMISSIONERS 29e Confrence internationale des commissaires à la protection de la vie prive In Detail: Cystic Fibrosis (1149 patients, 174 hospitals) BEFORE Protection 100% Samples In Repository AFTER Protection 0% Samples Re-identified