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Full-Text Articles in Physical Sciences and Mathematics
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Research Collection School Of Computing and Information Systems
As an important application of the Internet of Things (IoT) technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liu et al. proposed a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing, claiming to provide batch authentication, non-repudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and rigorous probability analysis indicates …
Beyond K-Anonymity: A Decision Theoretic Framework For Assessing Privacy Risk, Guy Lebanon, Monica Scannapieco, Mohamed Fouad, Elisa Bertino
Beyond K-Anonymity: A Decision Theoretic Framework For Assessing Privacy Risk, Guy Lebanon, Monica Scannapieco, Mohamed Fouad, Elisa Bertino
Cyber Center Publications
An important issue any organization or individual has to face when managing data containing sensitive information, is the risk that can be incurred when releasing such data. Even though data may be sanitized before being released, it is still possible for an adversary to reconstruct the original data using additional information thus resulting in privacy violations. To date, however, a systematic approach to quantify such risks is not available. In this paper we develop a framework, based on statistical decision theory, that assesses the relationship between the disclosed data and the resulting privacy risk. We model the problem of deciding …