Privacy-Preserving Assessment of Social Network Data Trustworthiness

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Author

Chenyun Dai, Fang-Yu Rao, Traian Marius Truta, Elisa Bertino

Tech report number

CERIAS TR 2012-08

Entry type

article

Abstract

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

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Date

2012 – 6 – 5

Key alpha

Dai

Publication Date

2012-06-05

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@Article{ Dai,
	title = "Privacy-Preserving Assessment of Social Network Data Trustworthiness",
	author = "Chenyun Dai, Fang-Yu Rao, Traian Marius Truta, Elisa Bertino",
	year = "2012",
	month = "6",
	day = "5",
	abstract = "Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.",
}