Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

By Zhipeng Cai, Zaobo He, Yingshu Li. IEEE Transactions on Dependable and Secure Computing, 2018

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Progress in Outlier Detection Techniques: A Survey (2019, background): “Other areas that are confronted with challenges include in application areas such as mobile social networks, security surveillance [18], [239], trajectory streams [19], and traffic management [20], [21].”
A survey on security and privacy issues of blockchain technology (2018, background): “The spectrum of blockchain applications range from financial, healthcare, automobile, risk management, Internet of things (IoT) to public and social services.”
Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs (2020, background): “They either consider the privacy preservation [33] or the utility maintenance [34] as a common task among participants.”
A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications (2017, background): “…conﬁdentiality, which aims to encrypt the data without being eavesdropped and inter-fered by nonauthorized users, privacy ensures that the user can only have some speciﬁc controls based on received data and cannot infer other valuable information from the received data [20], [106], [144], [159].”
Secure Deep Graph Generation with Link Differential Privacy (2020, background): “More importantly, even with careful anonymization, individual privacy is still at stake under collective attack models facilitated by the underlying graph structure [Zhang et al., 2019; Cai et al., 2018; Sun et al., 2018].”