Abstract

Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in ubiquitous computing settings. But today's schemes have problems such as loss of privacy, favoring retail monopolies, and with hampering diffusion of innovations. We propose an alternative model in which users control all of their log data. We describe an algorithm whereby a community of users can compute a public "aggregate" of their data that does not expose individual users' data. The aggregate allows personalized recommendations to be computed by members of the community, or by outsiders. The numerical algorithm is fast, robust and accurate. Our method reduces the collaborative filtering task to an iterative calculation of the aggregate requiring only addition of vectors of user data. Then we use homomorphic encryption to allow sums of encrypted vectors to be computed and decrypted without exposing individual data. We give verification schemes for all parties in the computation. Our system can be implemented with untrusted servers, or with additional infrastructure, as a fully peer-to-peer (P2P) system. 1

Citations

847	Oceanstore: An architecture for global-scale persistent storage - Kubiatowicz, Bindel, et al. - 2000
774	Empirical Analysis of Predictive Algorithms for Collaborative Filtering - Breese, Heckerman, et al. - 1998
574	A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation - Ben-Or, Goldwasser, et al. - 1988
388	An Algorithmic Framework for Performing Collaborative Filtering - Herlocker, Konstan, et al. - 1999
180	A Secure and Optimally Efficient Multi-Authority Election Scheme - Cramer, Gennaro, et al. - 1997
169	J.T.: Application of dimensionality reduction in recommender system – a case study - Sarwar, Karypis, et al. - 2000
162	A Threshold Cryptosystem without a Trusted Party - Pedersen - 1991
135	C.L.: Collaborative filtering by personality diagnosis: A hybrid memory-and-model-based approach - Penmnock, Horvitz, et al. - 2000
79	Diffusion of Innovations (fourth edition - Rogers - 1995
75	Computational Methods in Optimization - Polak - 1971
50	T.: Efficient multiparty computations secure against an adaptive adversary - Cramer, Damg˚ard, et al. - 1999
8	Damg˚ard: Zero Knowledge for Finite Field Arithmetic or: Can Zero Knowledge be for Free?, manuscript - Cramer, I - 1997
6	Jester 2.0: Evaluation of a new linear time collaborative filtering algorithm - Gupta, Digiovanni, et al. - 1999
1	Verifiable secret-sharing and multiparty protocols with honest majority - Rabin, Ben-Or - 1989