research-article

A lightweight privacy preserving SMS-based recommendation system for mobile users

Authors:

Elisa Baglioni,

Luca Becchetti,

Lorenzo Bergamini,

Luca Filipponi,

Andrea Vitaletti,

Giuseppe PersianoAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 191 - 198

https://doi.org/10.1145/1864708.1864745

Published: 26 September 2010 Publication History

Abstract

In this paper we propose a fully decentralized approach for recommending new contacts in the social network of mobile phone users. With respect to existing solutions, our approach is characterized by some distinguishing features. In particular, the application we propose does not assume any centralized coordination: it transparently collects and processes user information that is accessible in any mobile phone, such as the log of calls, the list of contacts or the inbox/outbox of short messages and exchanges it with other users. This information is used to recommend new friendships to other users. Furthermore, the information needed to perform recommendation is collected and exchanged between users in a privacy preserving way. Finally, information necessary to implement the application is exchanged transparently and opportunistically, by using the residual space in standard short messages occasionally exchanged between users. As a consequence, we do not ask users to change their habits in using SMS.

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Kolenbrander JHusmann EHenshaw CRheault EBoswell MMichaels A(2024)Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy ExperimentationJournal of Cybersecurity and Privacy10.3390/jcp40300264:3(546-571)Online publication date: 11-Aug-2024
https://doi.org/10.3390/jcp4030026
Xiao LLu QGuo F(2020)Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerceSustainability10.3390/su1207303612:7(3036)Online publication date: 10-Apr-2020
https://doi.org/10.3390/su12073036
Show More Cited By

Index Terms

A lightweight privacy preserving SMS-based recommendation system for mobile users
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

An YTan YSun XFerrari G(2024)Recommender System: A Comprehensive Overview of Technical Challenges and Social ImplicationsIECE Transactions on Sensing, Communication, and Control10.62762/TSCC.2024.8985031:1(30-51)Online publication date: 15-Oct-2024
https://doi.org/10.62762/TSCC.2024.898503
Kolenbrander JHusmann EHenshaw CRheault EBoswell MMichaels A(2024)Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy ExperimentationJournal of Cybersecurity and Privacy10.3390/jcp40300264:3(546-571)Online publication date: 11-Aug-2024
https://doi.org/10.3390/jcp4030026
Xiao LLu QGuo F(2020)Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerceSustainability10.3390/su1207303612:7(3036)Online publication date: 10-Apr-2020
https://doi.org/10.3390/su12073036
Sandhu RWeistroffer HStanley-Brown J(2019)Privacy Concerns and Remedies in Mobile Recommender Systems (MRSs)Information Systems: Research, Development, Applications, Education10.1007/978-3-030-29608-7_9(105-118)Online publication date: 8-Aug-2019
https://doi.org/10.1007/978-3-030-29608-7_9
Katarya RParihar A(2017)Study on privacy preserving recommender systems datasets2017 International Conference on Inventive Computing and Informatics (ICICI)10.1109/ICICI.2017.8365367(335-338)Online publication date: Nov-2017
https://doi.org/10.1109/ICICI.2017.8365367
Samanthula BJiang W(2016)Secure Multiset Intersection Cardinality and its Application to Jaccard CoefficientIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2015.241548213:5(591-604)Online publication date: 1-Sep-2016
https://doi.org/10.1109/TDSC.2015.2415482
Xu KYan Z(2016)Privacy Protection in Mobile Recommender Systems: A SurveySecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-319-49148-6_26(305-318)Online publication date: 10-Nov-2016
https://doi.org/10.1007/978-3-319-49148-6_26
Blundo CDe Cristofaro EGasti P(2014)EsPRESSOJournal of Computer Security10.5555/2597910.259791122:3(355-381)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.5555/2597910.2597911
Blundo CDe Cristofaro EGasti P(2014)EsPRESSO: Efficient privacy-preserving evaluation of sample set similarityJournal of Computer Security10.3233/JCS-13048222:3(355-381)Online publication date: 20-Mar-2014
https://doi.org/10.3233/JCS-130482
Costantino GMartinelli FSanti PWu JWang Y(2014)Privacy-Preserving Opportunistic NetworkingOpportunistic Mobile Social Networks10.1201/b17231-12(313-338)Online publication date: 17-Jul-2014
https://doi.org/10.1201/b17231-12
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