Privacy Preserving Data Mining within Anonymous Credentials

1. Problem definition and motivation • Context: Cryptographic anonymous credentials are very useful for privacy protection. There have been many anonymous credential schemes. • Problem: Anonymous credentials could be abused by malicious insiders to launch attacks without being held accountable (due to the anonymity shield). • Current generation of anonymous credentials have weak manageability (i.e., can be easily defeated or bypassed by sophisticated attackers). Moreover, they rely on out-of-band clues (which may be costly or impossible) indicating someone or something is suspicious. • Motivation: How can we eliminate the “dark side” of the “double-edged” sword, i.e., how can we manage the use of anonymous credentials without jeopardizing privacy of the good guys? • In particular, how can we extract knowledge or intelligence as if no anonymous credentials are involved (i.e., getting best of both worlds)? Privacy Preserving Data Mining within Anonymous Credentials A solution framework and preliminary results • Solution framework: Privacy preserving data mining within anonymous credentials • We propose privacy preserving data mining capabilities that serve as a necessary means for managing anonymous credentials in a full-fledged fashion. Such in-system clues of abuse eliminates the reliance on out-of-band information. Further investigations (e.g., anonymity revocation) Aggelos Kiayias (UConn) Shouhuai Xu (UTSA) Moti Yung (Google Inc. and Columbia U) Privacy preserving intelligence: Clues of abuse Anonymous transaction DB (no IDs!) Privacy preserving data mining engine (run by multiple trusted third parties) (Non)anonymous service provider Anonymous transactions User DB Users masked by the same public key Group-wise public key • Preliminary results: • The privacy preserving data mining capabilities can be seamlessly integrated into a large class of anonymous credential schemes. • Efficient and provably-secure cryptographic protocols for privacy preserving computation of some statistical functions (e.g., anonymized histograms for outlier detection). • Paper to appear in Proceedings of 6th International Conference on Security and Cryptography for Networks (SCN’08). • Future/ongoing work: • Enrich the privacy preserving data mining functionalities. • More efficient cryptographic protocols for the privacy preserving data mining functionalities.