Sean Peisert

ACM Distinguished Member

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Upcoming activities:

IEEE Security & Privacy (ongoing)

NSA SoS Best Paper Competition (annually, deadlines in April)

IEEE Cybersecurity Award for Practice (annually, deadlines in July)

IEEE S&P (Oakland) 2024 (May 20–23, 2024)

CSET 2024 (Aug. 2024)

NSPW 2024 Sept. 16–18, 2024)

NSF Cybersecurity Summit (Oct. 7–10 2024)



Recommendation Systems

This project was looking at the security of recommender systems. It evaluated vulnerabilities in relatively unstudied model-based recommendation systems in which recommendations are based on a model that relates ratings on one item to ratings on other items. Recommendation systems are a means of reducing "information overload" by filtering a potentially overwhelming number of options (such as all the products available from a seller) to identify those calculated to be of greatest interest. This project extends research on collaborative recommendation systems, which base recommendations for an individual on the preferences expressed by other people, by investigating the problem of malicious manipulation of these systems, for example, by an attacker attempting to influence the outcome with biased or faked rating profiles. Research suggests that a specific model-based systems exhibit much more resistant to recommendation attacks than memory-based systems in which recommendations are based on the principle of finding similar users or similar items. Moreover, this research investigated previously unknown attack methods that might be specifically effective against model-based recommendation systems.

Sponsor: National Science Foundation

Researchers involved:

Faculty: Graduate Students and Alumni:
  • Bhume Bhumitarana (UC Davis, Ph.D. 2009; → Faculty at Univ. of Technology, Thonburi, Thailand)
  • Michael Clifford (UC Davis)
  • Steven Templeton (UC Davis)

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Last modified: Sunday, 20-Oct-2013 19:38:03 PDT