Presentation is loading. Please wait.

Presentation is loading. Please wait.

Collaborative filtering with privacy Wim Verhaegh Aukje van Duijnhoven Jan Korst Pim Tuyls IPA herfstdagen, 23 November 2004.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "Collaborative filtering with privacy Wim Verhaegh Aukje van Duijnhoven Jan Korst Pim Tuyls IPA herfstdagen, 23 November 2004."— Presentation transcript:

1 Collaborative filtering with privacy Wim Verhaegh Aukje van Duijnhoven Jan Korst Pim Tuyls IPA herfstdagen, 23 November 2004

2 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20042 Privacy issue Personalization is key in Ambient Intelligence –requires user profiles Privacy risks of services –untrusted server –server gets hacked –server goes bankrupt Perform personalization on encrypted data –collaborative filtering

3 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20043 Overview Collaborative filtering system Privacy requirements CF method –calculation scheme (formulas & example) Encryption basics Encrypted CF method Item-based CF Conclusion

4 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20044 Collaborative filtering system System to recommend new content –recommend content that ‘similar users’ like database with ratings calculate similarities similarity values predict missing ratings music player user server sideuser side recommend

5 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20045 Security requirements Nobody may know users’ ratings –not even anonymously Nobody may know who rated what –not even anonymously Nobody may know who resembles who How to perform collaborative filtering?

6 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20046 Collaborative filtering methods Memory based –computes similarities and interpolates user based item based Model based –first uses rating database to build a model (e.g. extract basic rating profiles) –uses model for prediction Most approaches can be encrypted usersitems x x x x x x xx xx x x x x x x x x x xx x x x x x x x x x x x x x x x x x x

7 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20047 Memory-based CF with user similarities Two steps 1.determine similarities between users 2.predict missing ratings Step 1: Pearson correlation

8 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20048 Step 2: prediction E.g. weighted deviations from the average –similarities are weights

9 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 20049 Example Tea and coffee flavors –4 users –9 items (flavors) T1T2T3C1C2C3C4C5C6 Wim21144343 Jan1155443 Pim4552332 Aukje541322

10 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200410 Example Subtract averages flavorT1T2T3C1C2C3C4C5C6 Wim-0.8-1.8 1.2 0.21.20.2 Jan-2.3 1.7 0.7 -0.3 Pim0.61.6 -1.4-0.4 -1.4 Aukje2.21.2-1.80.2-0.8 T1T2T3C1C2C3C4C5C6 Wim-0.8-1.8 1.2 0.21.20.2 Jan-2.3 1.7 0.7 -0.3 Pim0.61.6 -1.4-0.4 -1.4 Aukje2.21.2-1.80.2-0.8 Compute similarities, e.g.

11 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200411 Example Use similarities to predict missing ratings similarityWimJanPimAukjeT3 Wim10.78-0.96-0.85-1.8 Jan0.781-0.74-0.77-2.3 Pim-0.96-0.7410.831.6 Aukje-0.85-0.770.831 Prediction for Aukje, tea T3

12 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200412 Public key encryption scheme: Paillier Generate keys –choose large random primes p, q (private) –calculate n = pq and a ‘generator’ g (public) Encrypt message m by with r random Homomorphism properties

13 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200413 Encrypted inner product User a: User b: User a encrypts vector and sends to b User b computes and sends back to a User a decrypts it to get inner product

14 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200414 Encrypted CF: correlation step Rewrite correlation as three inner products where Zeros to avoid contributions from in sums

15 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200415 Encrypted CF: correlation step Protocol Active user knows correlation values, but not to whom Server knows between whom, but not the correlation values active userserverother users copy

16 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200416 Encrypted CF: prediction step Rewrite Protocol –each user b adds random factor active userserverother users split

17 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200417 Memory-based CF with item similarities Similarities computed between items –compare rows in the matrix –similar formulas users items x x x x x x xx xx x x x x x x x x x xx x x x x x x x x x x x x x x x x x x

18 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200418 Memory-based CF with item similarities Similarities Predictions

19 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200419 Threshold Paillier Calculation of sums: use threshold encryption –key is shared among k users –decryption needs > t shares serverusers > t

20 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200420 Encrypted item-correlation step Rewrite correlation Protocol serverusers > t

21 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200421 Encrypted item-based prediction Rewrite prediction formula –item average: two sums –prediction: four inner products (server & user a) –protocols as before

22 Philips Research, Wim Verhaegh, IPA herfstdagen, 23 November 200422 Conclusion Collaborative filtering can be encrypted –various correlation and prediction formulas –various CF approaches More computations to be done at users’ sites –encryption and decryption –users have to be online Future work –protection against more complicated attacks –peer-to-peer solution

23

Download ppt "Collaborative filtering with privacy Wim Verhaegh Aukje van Duijnhoven Jan Korst Pim Tuyls IPA herfstdagen, 23 November 2004."

Similar presentations

RSA COSC 201 ST. MARY’S COLLEGE OF MARYLAND FALL 2012 RSA.

RSA COSC 201 ST. MARY’S COLLEGE OF MARYLAND FALL 2012 RSA.
CS470, A.SelcukCryptographic Authentication1 Cryptographic Authentication Protocols CS 470 Introduction to Applied Cryptography Instructor: Ali Aydin Selcuk.

CS470, A.SelcukCryptographic Authentication1 Cryptographic Authentication Protocols CS 470 Introduction to Applied Cryptography Instructor: Ali Aydin Selcuk.
ITIS 6200/8200. 2 Secure multiparty computation – Alice has x, Bob has y, we want to calculate f(x, y) without disclosing the values – We can only do.

ITIS 6200/ Secure multiparty computation – Alice has x, Bob has y, we want to calculate f(x, y) without disclosing the values – We can only do.
Jeff Howbert Introduction to Machine Learning Winter 2012 1 Collaborative Filtering Nearest Neighbor Approach.

Jeff Howbert Introduction to Machine Learning Winter Collaborative Filtering Nearest Neighbor Approach.
PRIVACY AND SECURITY ISSUES IN DATA MINING P.h.D. Candidate: Anna Monreale Supervisors Prof. Dino Pedreschi Dott.ssa Fosca Giannotti University of Pisa.

PRIVACY AND SECURITY ISSUES IN DATA MINING P.h.D. Candidate: Anna Monreale Supervisors Prof. Dino Pedreschi Dott.ssa Fosca Giannotti University of Pisa.
Item-based Collaborative Filtering Idea: a user is likely to have the same opinion for similar items [if I like Canon cameras, I might also like Canon.

Item-based Collaborative Filtering Idea: a user is likely to have the same opinion for similar items [if I like Canon cameras, I might also like Canon.
A PASS Scheme in Clouding Computing - Protecting Data Privacy by Authentication and Secret Sharing Jyh-haw Yeh Dept. of Computer Science Boise State University.

A PASS Scheme in Clouding Computing - Protecting Data Privacy by Authentication and Secret Sharing Jyh-haw Yeh Dept. of Computer Science Boise State University.
Rubi’s Motivation for CF  Find a PhD problem  Find “real life” PhD problem  Find an interesting PhD problem  Make Money!

Rubi’s Motivation for CF  Find a PhD problem  Find “real life” PhD problem  Find an interesting PhD problem  Make Money!
Memory-Based Recommender Systems : A Comparative Study Aaron John Mani Srinivasan Ramani CSCI 572 PROJECT RECOMPARATOR.

Memory-Based Recommender Systems : A Comparative Study Aaron John Mani Srinivasan Ramani CSCI 572 PROJECT RECOMPARATOR.
CSCE 715 Ankur Jain 11/16/2010. Introduction Design Goals Framework SDT Protocol Achievements of Goals Overhead of SDT Conclusion.

CSCE 715 Ankur Jain 11/16/2010. Introduction Design Goals Framework SDT Protocol Achievements of Goals Overhead of SDT Conclusion.
Lecture 14: Collaborative Filtering Based on Breese, J., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative.

Lecture 14: Collaborative Filtering Based on Breese, J., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative.
Secure communications Week 10 – Lecture 2. To summarise yesterday Security is a system issue Technology and security specialists are part of the system.

Secure communications Week 10 – Lecture 2. To summarise yesterday Security is a system issue Technology and security specialists are part of the system.
Secure Efficient Multiparty Computing of Multivariate Polynomials and Applications Dana Dachman-Soled, Tal Malkin, Mariana Raykova, Moti Yung.

Secure Efficient Multiparty Computing of Multivariate Polynomials and Applications Dana Dachman-Soled, Tal Malkin, Mariana Raykova, Moti Yung.
Mar 5, 2002Mårten Trolin1 Previous lecture More on hash functions Digital signatures Message Authentication Codes Padding.

Mar 5, 2002Mårten Trolin1 Previous lecture More on hash functions Digital signatures Message Authentication Codes Padding.
Responder Anonymity and Anonymous Peer-to-Peer File Sharing. by Vincent Scarlata, Brian Levine and Clay Shields Presentation by Saravanan.

Responder Anonymity and Anonymous Peer-to-Peer File Sharing. by Vincent Scarlata, Brian Levine and Clay Shields Presentation by Saravanan.
1 Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles The 3rd ACM Conference on Recommender Systems, New.

1 Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles The 3rd ACM Conference on Recommender Systems, New.
Recommendations via Collaborative Filtering. Recommendations Relevant for movies, restaurants, hotels…. Recommendation Systems is a very hot topic in.

Recommendations via Collaborative Filtering. Recommendations Relevant for movies, restaurants, hotels…. Recommendation Systems is a very hot topic in.
Efficient fault-tolerant scheme based on the RSA system Author: N.-Y. Lee and W.-L. Tsai IEE Proceedings Presented by 詹益誌 2004/03/02.

Efficient fault-tolerant scheme based on the RSA system Author: N.-Y. Lee and W.-L. Tsai IEE Proceedings Presented by 詹益誌 2004/03/02.
An Authentication Service Against Dishonest Users in Mobile Ad Hoc Networks Edith Ngai, Michael R. Lyu, and Roland T. Chin IEEE Aerospace Conference, Big.

An Authentication Service Against Dishonest Users in Mobile Ad Hoc Networks Edith Ngai, Michael R. Lyu, and Roland T. Chin IEEE Aerospace Conference, Big.
Birthday Attack on Efficient and Anonymous Buyer-Seller Watermarking Protocol BY Qurat-ul-Ain M. Mahboob Yasin COMSATS Institute of Information Technology,

Birthday Attack on Efficient and Anonymous Buyer-Seller Watermarking Protocol BY Qurat-ul-Ain M. Mahboob Yasin COMSATS Institute of Information Technology,

Similar presentations

Ads by Google