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Security Risk Estimation of Social Network Privacy Issue

Published: 24 November 2017 Publication History

Abstract

Users in social network are confronted with the risk of privacy leakage while sharing information with friends whose privacy protection awareness is poor. This paper proposes a security risk estimation framework of social network privacy, aiming at quantifying privacy leakage probability when information is spread to the friends of target users' friends. The privacy leakage probability in information spreading paths comprises Individual Privacy Leakage Probability (IPLP) and Relationship Privacy Leakage Probability (RPLP). IPLP is calculated based on individuals' privacy protection awareness and the trust of protecting others' privacy, while RPLP is derived from relationship strength estimation. Experiments show that the security risk estimation framework can assist users to find vulnerable friends by calculating the average and the maximum privacy leakage probability in all information spreading paths of target user in social network. Besides, three unfriending strategies are applied to decrease risk of privacy leakage and unfriending the maximum degree friend is optimal.

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  • (2024)A Systematic Mapping Study on Social Network Privacy: Threats and SolutionsACM Computing Surveys10.1145/364508656:7(1-29)Online publication date: 9-Apr-2024

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cover image ACM Other conferences
ICCNS '17: Proceedings of the 2017 7th International Conference on Communication and Network Security
November 2017
125 pages
ISBN:9781450353496
DOI:10.1145/3163058
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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  • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 November 2017

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Author Tags

  1. privacy leakage
  2. relationship strength
  3. security estimation
  4. trust
  5. unfriending

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  • (2024)A Systematic Mapping Study on Social Network Privacy: Threats and SolutionsACM Computing Surveys10.1145/364508656:7(1-29)Online publication date: 9-Apr-2024

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