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

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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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Notes

  1. “If you look at instant messaging, e-mail or even social networking, they don’t have the ubiquity and the reach to replace messaging” - Bill Dudley, Sybase 365’s group director for product management.

  2. Jyngle closed in August 2009.

  3. An alternative solution is that A sends to B part of its sketch, compatibly with the available space in the SMS message body. This solution requires bookkeeping both at A and B, to keep track of the portions of \(sk(A)\) still missing at B. In fact, the former solution can be more easily implemented than the latter and it requires no additional data structures.

  4. For more details, also see http://www.rsa.com/rsalabs/node.asp?id=2098.

  5. The dataset is publicly available for research purposes at http://odysseas.calit2.uci.edu/doku.php/public:online_social_networks#facebook_social_graph.

  6. Filtering contact list entries based on contact frequency should be done cautiously, since less frequent or rare contacts tend to provide more similarity information. This is also one of the main ideas behind the definition of Adamic–Adar similarity coefficient.

  7. The threshold is represented as a label over each point in the scatterplot.

  8. This is the reason why only one point of the 10 hash algorithm is represented on the scatterplot.

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Authors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    L. Becchetti, L. Bergamini, U. M. Colesanti, L. Filipponi & A. Vitaletti

  2. University of Salerno, Salerno, Italy

    Ginseppe Persiano

Authors
  1. L. Becchetti
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  2. L. Bergamini
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  3. U. M. Colesanti
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  4. L. Filipponi
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  5. Ginseppe Persiano
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  6. A. Vitaletti
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Corresponding author

Correspondence to L. Becchetti.

Additional information

Partially supported by PRIN 2008 research project COGENT, funded by the Italian Ministry of University and Research.

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Becchetti, L., Bergamini, L., Colesanti, U.M. et al. A lightweight privacy preserving SMS-based recommendation system for mobile users. Knowl Inf Syst 40, 49–77 (2014). https://doi.org/10.1007/s10115-013-0632-z

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