Privacy Implications of Data Disclosure
Almost every interaction with technology creates digital traces, from the cell tower used to route mobile calls to the vendor recording a credit card transaction; from the photographs we take, to the “status updates” we post online. The idea that these traces can all be merged and connected is both fascinating and unsettling. In this series of talks, we analyze privacy implications of data disclosure from a theoretical and practical perspective.
More specifically, we first discuss few well-known privacy leakages cases and different de-anonymization algorithms. Then we present several anonymization techniques with a particular focus on differential privacy. Finally we discuss new algorithmic challenges that arise from designing privacy-aware systems.
- Arvind Narayanan, Vitaly Shmatikov: Robust De-anonymization of Large Sparse Datasets. IEEE Symposium on Security and Privacy 2008: 111-125
- Arvind Narayanan, Vitaly Shmatikov: De-anonymizing Social Networks. IEEE Symposium on Security and Privacy 2009: 173-187
- Nitish Korula, Silvio Lattanzi: An efficient reconciliation algorithm for social networks. PVLDB 7(5): 377-388 (2014)
- Cynthia Dwork, Aaron Roth: The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science 9(3-4): 211-407 (2014)
- Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, Vahab S. Mirrokni: Efficient Algorithms for Public-Private Social Networks. KDD 2015: 139-148
- Christopher J. Riederer, Yunsung Kim, Augustin Chaintreau, Nitish Korula, Silvio Lattanzi: Linking Users Across Domains with Location Data: Theory and Validation. WWW 2016: 707-719