Corpus ID: 17919466

Privacy Protection in Social Networking Services

@inproceedings{Aalto2010PrivacyPI,
  title={Privacy Protection in Social Networking Services},
  author={Daoyuan Li Aalto},
  year={2010}
}
As social networking services become increasingly popular, more and more attacks against users’ private information are reported. As a result, privacy protection becomes an important concern among users. Previous research has produced many different approaches to deal with privacy control in different social networking sites. In this paper, we make a survey on different approaches proposed to tackle the privacy issue in social networking sites. In particular, we put current approaches into… Expand

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References

SHOWING 1-10 OF 21 REFERENCES
Privacy preserving social networking through decentralization
TLDR
This paper points to the centralized architecture of existing on-line social networks as the key privacy issue and suggests a solution that aims at avoiding any centralized control and leverages the trust relationships that are part of the social network application itself. Expand
Inferring privacy policies for social networking services
TLDR
A machine learning approach is used to extract automatically privacy settings based on the policy that information produced within a social context should remain in that social context, both to ensure privacy as well as maximising utility. Expand
Privacy suites: shared privacy for social networks
TLDR
A new paradigm is proposed which allows users to easily choose "suites" of privacy settings which have been specified by friends or trusted experts, only modifying them if they wish, which could dramatically increase the privacy protection that most users experience with minimal time investment. Expand
The Privacy Jungle: On the Market for Data Protection in Social Networks
TLDR
The market for privacy in social networks is dysfunctional in that there is significant variation in sites’ privacy controls, data collection requirements, and legal privacy policies, but this is not effectively conveyed to users. Expand
Collective privacy management in social networks
TLDR
This paper proposes a solution that offers automated ways to share images based on an extended notion of content ownership that promotes truthfulness, and that rewards users who promote co-ownership, and shows that supporting these type of solutions is not feasible, but can be implemented through a minimal increase in overhead to end-users. Expand
Privacy wizards for social networking sites
TLDR
A template for the design of a social networking privacy wizard based on an active learning paradigm called uncertainty sampling, which is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools. Expand
A case for P2P infrastructure for social networks - opportunities & challenges
  • S. Buchegger, Anwitaman Datta
  • Computer Science
  • 2009 Sixth International Conference on Wireless On-Demand Network Systems and Services
  • 2009
TLDR
This paper is an attempt to identify the core functionalities necessary to build social networking applications and services, and the research challenges in realizing them in a decentralized setting, and presents its own approach at realizing peer-to-peer social networks. Expand
Inferring Privacy Information from Social Networks
TLDR
The experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships, and even in a society where most people hide their attributes, it is still possible to infer privacy information. Expand
Inferring private information using social network data
TLDR
This paper explores how to launch inference attacks using released social networking data to predict undisclosed private information about individuals and the effectiveness of possible sanitization techniques that can be used to combat such inference attacks under different scenarios. Expand
PeerSoN: P2P social networking: early experiences and insights
TLDR
This paper describes the description of the prototype built for the P2P infrastructure for social networks, as a first step without the encryption part, and shares early experiences from the prototype and insights gained since first outlining the challenges and possibilities of decentralized alternatives to OSNs. Expand
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