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Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

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Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles Reza Shokri Pedram Pedarsani George Theodorakopoulos – PowerPoint PPT presentation

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Title: Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles


1
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
2
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.
3
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
4
Outline
  • Profile Aggregation
  • Aggregation Methods
  • Evaluation

5
Profile Aggregation
items
2
4
4
3
3
1
5
2
5
5
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

6
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

7
Online Profiles vs. Actual Profiles
8
Aggregation Methods
  • How many items to aggregate?
  • Which items to aggregate?
  • Similarity-based Aggregation
  • (Similarity The Pearsons 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
9
Evaluation Metrics
  • Privacy Gain
  • Accuracy Loss

10
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.
11
Accuracy Loss
The bipartite graph that contains actual
ratings The bipartite graph available to the
server
12
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

13
Privacy Gain
Similarity-based Random Selection
(SRS) Similarity-based Minimum Rating Frequency
(SMRF)
14
Accuracy Loss
Similarity-based Random Selection
(SRS) Similarity-based Minimum Rating Frequency
(SMRF)
15
Tradeoff between Privacy and Accuracy
16
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

17
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
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