Privacy-Enhanced Collaborative Filtering Privacy-Enhanced Personalization workshop July 25, 2005, Edinburgh, Scotland Shlomo Berkovsky1, Yaniv Eytani1, Tsvi Kuflik2, Francesco Ricci3

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Privacy-EnhancedCollaborative FilteringPrivacy-
Enhanced Personalization workshopJuly 25, 2005,
Edinburgh, ScotlandShlomo Berkovsky1, Yaniv
Eytani1, Tsvi Kuflik2, Francesco Ricci3

• 1Computer Science Department, University of
  Haifa, Israel 2Management Information Systems
  Department, University of Haifa,
  Israel3ITC-irst, Trento, Italy
• This work is supported by the collaboration
  project between the University of Haifa and
  ITC/irst

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Outline

• Collaborative Filtering (CF)
• Distributed Privacy-Enhanced CF
• Experimental Results
• Open Questions

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Collaborative Filtering (CF)

• Based on assumption that people with similar
  taste prefer similar items
• 3 basic stages
• Similarity computation (Pearson correlation,
  Cosine, Mean-Squared Difference)
• Neighborhood formation (K-Nearest Neighbors)
• Personalized prediction generation (Weighted
  average of neighbors ratings)

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CF and Privacy

• Service providers collect information about their
  users
• Personalization raises the issue of privacy
• Prior works
• Canny P2P-based CF, users communities,
  encryption
• PolatDu partitioning of CF data, data
  perturbation techniques

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Distributed Privacy-Enhanced CF

• Combines the approaches of Canny and PolatDu
• Distributed and decentralized organization of
  users maintaining their personal profiles

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Recommendation Generation

• A user sends his profile and requests a
  recommendation
• Individual users independently decide whether to
  respond to the request
• The responder locally computes and sends
  similarity and his prediction
• The requesting user collects the responses,
  builds the neighborhood and generates the
  personalized prediction

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Privacy through Obfuscation

• User profile might be revealed by malicious
  attacker through multiple requests
• Privacy is increased by obfuscating parts of user
  profiles
• Basic question What portion of user profile can
  be obfuscated while continuing to generate
  accurate recommendations?

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Experimental Setting

• Part of Jester dataset of jokes ratings (-10 ..
  10)
• Dense dataset of 1024 users x 100 jokes
• 3 obfuscation policies
• Default(x) replace the ratings with x
• Uniform replace the ratings with random values
  chosen uniformly in the scope of ratings
• Bell_curve replace the ratings with random
  values chosen according to the distribution of
  real ratings in the dataset (bell curve
  distribution)

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Experimental Results
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Open Questions

• Will these results be true for other datasets?
• Sparse datasets, e.g. MovieLens
• Extreme ratings, e.g. edges of the bell curve
• Will our approach scale under an organized attack
  of multiple malicious users?

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Open Questions

• Can the profile of the active user be also
  obfuscated to increase privacy?
• Can just a portion of user profile be
  communicated to decrease communication costs and
  to improve scalability?

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Q A
Thank You!
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Question

• What happens if we simply give a random
  recommendation?