Security and Privacy in Communication Networks. 11th International Conference, SecureComm 2015, Dallas, TX, USA, October 26-29, 2015, Revised Selected Papers

Research Article

Resource Efficient Privacy Preservation of Online Social Media Conversations

  • @INPROCEEDINGS{10.1007/978-3-319-28865-9_13,
        author={Indrajeet Singh and Masoud Akhoondi and Mustafa Arslan and Harsha Madhyastha and Srikanth Krishnamurthy},
        title={Resource Efficient Privacy Preservation of Online Social Media Conversations},
        proceedings={Security and Privacy in Communication Networks. 11th International Conference, SecureComm 2015, Dallas, TX, USA, October 26-29, 2015, Revised Selected Papers},
        proceedings_a={SECURECOMM},
        year={2016},
        month={2},
        keywords={},
        doi={10.1007/978-3-319-28865-9_13}
    }
    
  • Indrajeet Singh
    Masoud Akhoondi
    Mustafa Arslan
    Harsha Madhyastha
    Srikanth Krishnamurthy
    Year: 2016
    Resource Efficient Privacy Preservation of Online Social Media Conversations
    SECURECOMM
    Springer
    DOI: 10.1007/978-3-319-28865-9_13
Indrajeet Singh1, Masoud Akhoondi1, Mustafa Arslan2, Harsha Madhyastha2, Srikanth Krishnamurthy1,*
  • 1: UC Riverside
  • 2: University of Michigan
*Contact email: krish@cs.ucr.edu

Abstract

On today’s online social networks (OSNs), users need to reveal their content and their sharing patterns to a central provider. Though there are proposals for decentralized OSNs to protect user privacy, they have paid scant attention to optimizing the cost borne by users or hiding their sharing patterns. In this paper, we present , a decentralized OSN architecture, designed explicitly with the goal of hiding sharing patterns while minimizing users’ costs. In doing so, tackles three key challenges: 1) it enables timely and consistent sharing of content, 2) it guarantees the confidentiality of posted private content, and 3) it hides sharing patterns from untrusted cloud service providers and users outside a private group. With extensive analyses of using traces of shared content on Facebook, we estimate that the cost borne per user will be less than $5 per month for over 90% of users. Our prototype implementation of demonstrates that it only adds minimal overhead to content sharing.