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

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.

[1]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[2]  Walter Willinger,et al.  Sampling Techniques for Large, Dynamic Graphs , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[3]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[4]  Walter Willinger,et al.  On unbiased sampling for unstructured peer-to-peer networks , 2009, TNET.

[5]  Ahmad-Reza Sadeghi,et al.  Efficient Privacy-Preserving Face Recognition , 2009, ICISC.

[6]  Songwu Lu,et al.  A study of the short message service of a nationwide cellular network , 2006, IMC '06.

[7]  Alan M. Frieze,et al.  Min-wise independent permutations (extended abstract) , 1998, STOC '98.

[8]  Luca Becchetti,et al.  A lightweight privacy preserving SMS-based recommendation system for mobile users , 2010, RecSys.

[9]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[10]  Chanathip Namprempre,et al.  The One-More-RSA-Inversion Problems and the Security of Chaum's Blind Signature Scheme , 2003, Journal of Cryptology.

[11]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[12]  A. Crofts,et al.  Structure and function of the -complex of , 1992 .

[13]  Fan Chung Graham,et al.  A random graph model for massive graphs , 2000, STOC '00.

[14]  Robin I. M. Dunbar.  Grooming, Gossip and the Evolution of Language , 1996 .

[15]  Stefan Katzenbeisser,et al.  Protection and Retrieval of Encrypted Multimedia Content: When Cryptography Meets Signal Processing , 2007, EURASIP J. Inf. Secur..

[16]  David W. McDonald,et al.  Social matching: A framework and research agenda , 2005, TCHI.

[17]  Emiliano De Cristofaro,et al.  Fast and Private Computation of Set Intersection Cardinality , 2011, IACR Cryptol. ePrint Arch..

[18]  Chris Clifton,et al.  Efficient privacy-preserving similar document detection , 2010, The VLDB Journal.

[19]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[20]  Carl Pomerance,et al.  The Development of the Number Field Sieve , 1994 .

[21]  Francesco Bonchi,et al.  Cold start link prediction , 2010, KDD.

[22]  Vincenzo Piuri,et al.  Privacy-preserving fingercode authentication , 2010, MM&Sec '10.

[23]  PentlandAlex,et al.  Reality mining: sensing complex social systems , 2006 .

[24]  Piotr Indyk,et al.  A small approximately min-wise independent family of hash functions , 1999, SODA '99.

[25]  Geoffrey Zweig,et al.  Syntactic Clustering of the Web , 1997, Comput. Networks.

[26]  Alan M. Frieze,et al.  Min-Wise Independent Permutations , 2000, J. Comput. Syst. Sci..

[27]  Michael R. Lyu,et al.  MatchSim: a novel similarity measure based on maximum neighborhood matching , 2012, Knowledge and Information Systems.

[28]  Fan Chung Graham,et al.  Random evolution in massive graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[29]  Charis M. Cussins.  Grooming, gossip, and the evolution of language , 1998 .

[30]  Minas Gjoka,et al.  Practical Recommendations on Crawling Online Social Networks , 2011, IEEE Journal on Selected Areas in Communications.

[31]  Rasmus Pagh,et al.  Finding associations and computing similarity via biased pair sampling , 2009, Knowledge and Information Systems.

[32]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[33]  Alan M. Frieze,et al.  Min-Wise Independent Linear Permutations , 2000, Electron. J. Comb..

[34]  Bradley N. Miller,et al.  PocketLens: Toward a personal recommender system , 2004, TOIS.

[35]  Marina Blanton,et al.  Secure and Efficient Protocols for Iris and Fingerprint Identification , 2011, ESORICS.

[36]  Emiliano De Cristofaro,et al.  EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity , 2012, DPM/SETOP.

[37]  Padraig Cunningham,et al.  A Taxonomy of Similarity Mechanisms for Case-Based Reasoning , 2009, IEEE Transactions on Knowledge and Data Engineering.