PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
November 7, 20052,033 citations

Information revelation and privacy in online social networks

View Full Paper
RGRalph GrossAAAlessandro Acquisti

Key Points

  • To examine patterns of personal data disclosure and the adoption of privacy settings among online social network users to determine privacy vulnerabilities.
  • Analyzed online profile disclosure patterns and privacy configuration usage among >4,000 Carnegie Mellon University students on a collegiate social networking platform.
  • Identified and evaluated potential attack vectors against user privacy based on the types of revealed personal information.
  • Users routinely disclose extensive personal information that is accessible across broad networks and to potential strangers.
  • Only a minimal percentage of users alter the default, highly permeable privacy settings to restrict profile access.

Abstract

Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Gross et al. (2005) studied this question.

synapsesocial.com/papers/6a0ec369aa1655e5fb22c3dehttps://doi.org/10.1145/1102199.1102214
Ask AI
Helpful
Bookmark
Share
View Full Paper