| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 99 === Privacy in publishing social network data is always an important concern. Nowadays most prior privacy protection techniques focus on static social networks. However, there are additional privacy disclosures in dynamic social networks due to the sequential publications. In this thesis, we first show that the risks of vertex or community re-identification exist in a dynamic social network, even if the network published at each time instance is protected by a static anonymity scheme. To prevent vertex and community re-identification in a dynamic social network, we develop novel dynamic k^w-structural diversity anonymity, where w is the time that an adversary can monitor a victim. This scheme extends the k-structural diversity anonymity to a dynamic scenario. We present a heuristic method to anonymize the networks to satisfy the proposed privacy scheme. The evaluations on both real and synthetic data sets show that our approach can retain much of the characteristic of the networks while confirming the privacy protection.
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