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Privacy-preserving demographic filtering

Published: 23 April 2006 Publication History

Abstract

The use of recommender systems in e-commerce to guide customer choices presents a privacy protection problem that is twofold. We seek to protect the privacy interests of customers by trying to keep private their identity and demographic characteristics, and possibly also their buying preferences and behaviour. This can be desirable even if anonymity is used. Furthermore, we want to protect the commercial interests of the e-commerce service providers by allowing them to make recommendations as accurate as possible, without unnecessarily revealing valuable information they have legitimately accumulated, such as market trends, to third parties.In this paper, we concentrate on recommender systems based on demographic filtering, which make recommendations based on feedback of previous users of similar demographic characteristics (such as age, sex, level of education, wealth, geographical location, etc.). We propose a system called ALAMBIC, which adequately achieves the above privacy-protection objectives in this kind of recommender systems. Our system is based on a semi-trusted third party in which the users need only have limited confidence. A main originality of our approach is to split user data between that party and the service provider in such a way that neither can derive sensitive information from their share alone.

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cover image ACM Conferences
SAC '06: Proceedings of the 2006 ACM symposium on Applied computing
April 2006
1967 pages
ISBN:1595931082
DOI:10.1145/1141277
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 April 2006

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Author Tags

  1. architecture
  2. clustering
  3. demographic filtering
  4. e-commerce
  5. privacy protection
  6. recommender system
  7. semi-trusted third party

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  • (2023)When Recommender Systems Snoop into Social Media, Users Trust them Less for Health AdviceProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581123(1-14)Online publication date: 19-Apr-2023
  • (2023)Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature ReviewMachine Intelligence for Smart Applications10.1007/978-3-031-37454-8_2(21-59)Online publication date: 25-Aug-2023
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