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Private Profile Matching for Mobile Social Networks Based on Fuzzy Extractors

5 pagesPublished: March 26, 2017

Abstract

Interacting with geographically proximate users who present similar interests and preferences is a key service offered by mobile social networks which leads to the creation of new connections that combine physical and social closeness. Usually these interactions are based on social profile matching where users publish their preferences and attributes to enable the search for a similar profile. Such public search would result in the leakage of sensitive or identifiable information to strangers who are not always potential friends. As a consequence this promising feature of mobile social networking may cause serious privacy breaches if not addressed properly. Most existent work relies on homomorphic encryption for privacy preservation during profile matching, while we propose in this paper a novel approach based on the fuzzy extractor which performs private matching of two sets and reveals them only if they overlap considerably. Our scheme achieves a desirable trade off between security and complexity.

Keyphrases: Fuzzy Extractor, Mobile Social Networks, Privacy, proximity, social profile

In: Mohamed Mosbah and Michael Rusinowitch (editors). SCSS 2017. The 8th International Symposium on Symbolic Computation in Software Science 2017, vol 45, pages 63--67

Links:
BibTeX entry
@inproceedings{SCSS2017:Private_Profile_Matching_for,
  author    = {Jaweher Zouari and Mohamed Hamdi and Tai-Hoon Kim},
  title     = {Private Profile Matching for Mobile Social Networks Based on Fuzzy Extractors},
  booktitle = {SCSS 2017. The 8th International Symposium on Symbolic Computation in Software Science 2017},
  editor    = {Mohamed Mosbah and Michael Rusinowitch},
  series    = {EPiC Series in Computing},
  volume    = {45},
  pages     = {63--67},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/MdJ9},
  doi       = {10.29007/st23}}
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