research-article

User Vulnerability and Its Reduction on a Social Networking Site

Authors:

Pritam Gundecha,

Geoffrey Barbier,

Jiliang Tang,

Huan LiuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 9, Issue 2

Article No.: 12, Pages 1 - 25

https://doi.org/10.1145/2630421

Published: 23 September 2014 Publication History

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Abstract

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the continuous expansion of a user’s social network, privacy settings alone are often inadequate to protect a user’s profile. In this research, we aim to address some critical issues related to privacy protection: (1) How can we measure and assess individual users’ vulnerability? (2) With the diversity of one’s social network friends, how can one figure out an effective approach to maintaining balance between vulnerability and social utility? In this work, first we present a novel way to define vulnerable friends from an individual user’s perspective. User vulnerability is dependent on whether or not the user’s friends’ privacy settings protect the friend and the individual’s network of friends (which includes the user). We show that it is feasible to measure and assess user vulnerability and reduce one’s vulnerability without changing the structure of a social networking site. The approach is to unfriend one’s most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him or her would significantly reduce one’s own social status. We formulate this novel problem as vulnerability minimization with social utility constraints. We formally define the optimization problem and provide an approximation algorithm with a proven bound. Finally, we conduct a large-scale evaluation of a new framework using a Facebook dataset. We resort to experiments and observe how much vulnerability an individual user can be decreased by unfriending a vulnerable friend. We compare performance of different unfriending strategies and discuss the security risk of new friend requests. Additionally, by employing different forms of social utility, we confirm that the balance between user vulnerability and social utility can be practically achieved.

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Ma CYuan LHan LDing MBhaskar RLi J(2023)Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313704735:4(3619-3631)Online publication date: 1-Apr-2023
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Index Terms

User Vulnerability and Its Reduction on a Social Networking Site
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. Data management systems
    1. Database administration

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Reviews

Reviewer: Amos O Olagunju

Social media users sometimes expose their friends to confidentiality and safety breaches. To what extent are companionable users vulnerable to security breaches in social networking sites__?__ How should access to social networks and security risks be balanced for individual friendly users__?__ Gundecha and others investigate these fascinating questions. The theory of vulnerability and its reduction in social networks in this paper is predicated on privacy settings that reveal the personal information of users, and unintended consequential actions by users and friends that reveal information about other friends and family members. The individual (I-index) vulnerability of each attribute in the profile of a user is the degree to which the user enables it to be visible to others in a social network. A user poses the risk of divulging an attribute of friendly users in a profile community (C-index) to the extent to which all other users of a social network can trace the attribute. The nonvisible privacy or visible publicity (P-index) vulnerability is a measure of the degree to which the users protect themselves and friends in a social network. The vulnerability (V-index) of users relies on their own privacy settings, as well as those of their friends and families in a social network. The authors use the pertinent information from the I-index, C-index, P-index, and V-index of users to derive theorems and equations for computing the most vulnerable friends to defriend, to maximize vulnerability and minimize the losses of each user in social networking sites. They perform a variety of experiments with a Facebook dataset to investigate the accuracy of the vulnerability reduction measures and algorithms. The results reveal that the proposed alternative strategies for defriending at least the most vulnerable friend would make all social contact users at least more secure to vulnerabilities. Undeniably, shoddy users who make new friends on social networks can become more vulnerable. The authors provide numerous approaches for pinpointing the utmost number of susceptible friends to defriend, to reduce vulnerability and the risk of losing social network contacts at suitable levels. I strongly encourage all managers of social networking sites to use the insightful ideas in this paper to investigate user vulnerabilities across networks. Online Computing Reviews Service

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 9, Issue 2

November 2014

193 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2672614

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2014

Accepted: 01 May 2014

Revised: 01 March 2014

Received: 01 August 2012

Published in TKDD Volume 9, Issue 2

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Office of Naval Research
Office of the Secretary of Defense
Army Research Office
Air Force Office of Scientific Research
DefenseWide/PE0601120D8Z National Defense Education Program (NDEP)/BA-1
Basic Research; SMART Program Office, www.asee.org/fellowships/smart

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