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http://lca.epfl.ch/privacy. Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles. Reza Shokri Pedram Pedarsani George Theodorakopoulos Jean-Pierre Hubaux. The 3rd ACM Conference on Recommender Systems, New York City, NY, USA, October 22-25, 2009.

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preserving privacy in collaborative filtering through distributed aggregation of offline profiles

http://lca.epfl.ch/privacy

Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

Reza Shokri

Pedram Pedarsani

George Theodorakopoulos

Jean-Pierre Hubaux

The 3rd ACM Conference on Recommender Systems, New York City, NY, USA, October 22-25, 2009

privacy in recommender systems
Privacy in Recommender Systems
  • Untrusted Server
    • Tracking users’ activities
  • Publishing Users’ Profiles
    • Re-identification attacks on anonymous datasets

A. Narayanan and V. Shmatikov. Robust de-anonymization of large sparse datasets. In IEEE Symposium on Security and Privacy, 2008.

problem statement
Problem Statement
  • Improving users privacy with minimum imposition of accuracy loss on the recommendations
    • Centralized recommender system
    • Contact between users
    • Distributed privacy preserving mechanism
  • Distributed aggregation of users’ profiles
    • Users hide the items they have actually rated through adding items rated by other users to their profile

Proposed Solution

outline
Outline
  • Profile Aggregation
  • Aggregation Methods
  • Evaluation
profile aggregation
Profile Aggregation

items

4

2

5

1

3

3

4

5

5

2

3

3

4

2

1

ratings

Alice

Bob

  • Each user gives a subset of his items to his contact peer
  • Thus, users profiles are aggregated after the contact
system model
System Model

Online profile

contact

synchronization

Offline profile

  • Actual Profile: Set of items rated by a user
  • Offline Profile: Actual profile + aggregated items
  • Online Profile: The latest synchronized offline profile on the server
online profiles vs actual profiles

Actual profile of users

Online profile of users

Online Profiles vs. Actual Profiles
aggregation methods
Aggregation Methods
  • How many items to aggregate?
  • Which items to aggregate?

Similarity-based Aggregation

(Similarity: The Pearson’s correlation coefficient)

    • Random Selection (SRS)
    • Minimum Rating Frequency (SMRF)

(rating frequency: percentage of users that have rated an item)

IMDB: 167,237 votes

IMDB: 1,625 votes

evaluation metrics
Evaluation Metrics
  • Privacy Gain
  • Accuracy Loss
privacy gain

number of users

actual profile of user ‘u’

rating frequency of item ‘i’

online profile of user ‘u’

Weight of items added by aggregation

Weight of items in online profile

Privacy Gain
  • Privacy: How difficult is for the server to guess the users’ actual profiles, having access to their online profiles

Intuition: Structural difference of two graphs (online and actual) viewed as difference between correspondent edges

R. Myers, R. C. Wilson, and E. R. Hancock. Bayesian graph edit distance. IEEE Trans. Pattern Anal. Mach. Intell., 22(6), 2000.

accuracy loss
Accuracy Loss
  • The bipartite graph that contains actual ratings
  • The bipartite graph available to the server
experiment
Experiment
  • Simulation on randomly chosen profiles
    • From the Netflix prize dataset
    • 300 users
    • Average: 30000 ratings and 2500 items in each experiment
  • Memory-based CF: user-based
  • Testing set: 10% of the actual ratings of each user
  • Users select their contact peers at random
  • Aggregation methods
    • Union
    • SRS
    • SMRF
privacy gain1
Privacy Gain
  • Similarity-based Random Selection (SRS)
  • Similarity-based Minimum Rating Frequency (SMRF)
accuracy loss1
Accuracy Loss
  • Similarity-based Random Selection (SRS)
  • Similarity-based Minimum Rating Frequency (SMRF)
conclusion
Conclusion
  • A novel method for privacy preservation in collaborative filtering recommendation systems
  • Protection of users privacy against an untrusted server
  • Considerably improving users privacy with minimum effect on recommendations accuracy by aggregating users’ profiles based on their similarities
  • Proposed method can also be used on protecting privacy of users in published datasets
future work

http://lca.epfl.ch/privacy

Future Work
  • The evaluation of the mechanism can be improved by considering more realistic contact pattern between users, e.g., users friendship in a social network, or physical vicinity
  • We would like to evaluate the practical implication of the method on the maintenance of the profiles