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Erschienen in:
Empowering the Knowledge Worker: End-User Software Engineering in Knowledge Management Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System Architecture

verfasst von : Alexander Smirnov, Andrew Ponomarev

Erschienen in: Enterprise Information Systems

Verlag: Springer International Publishing

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Abstract

Recommendation systems are currently widely used in domains where abundance of choice is conjoined with its subjective nature (books, movies, trips, etc.). Most of the modern recommendation systems are centralized. Although the centralized recommendation system design has some significant advantages, it also bears two primary disadvantages: the necessity for users to share their preferences and a single point of failure. This paper follows user-centric approach to distributed recommendation system design, and proposes an architecture of a collaborative peer-to-peer recommendation system with limited preferences’ disclosure. Privacy in the proposed design is provided by the fact that exact user preferences are never shared together with the user identity. To achieve that, the proposed architecture employs a locality-sensitive hashing of user preferences and an anonymized distributed hash table approach to peer-to-peer design.

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Vorheriges Kapitel The Development of Information Models and Methods of University Scientific Knowledge Management
Nächstes Kapitel An Approach to a Laser-Touchscreen System
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Metadaten
Titel
Locality-Sensitive Hashing for Distributed Privacy-Preserving Collaborative Filtering: An Approach and System Architecture
verfasst von
Alexander Smirnov
Andrew Ponomarev
Copyright-Jahr
2015
Buch
Enterprise Information Systems

Print ISBN: 978-3-319-29132-1

Electronic ISBN: 978-3-319-29133-8

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-29133-8_22