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- W4308191331 abstract "Abstract The increasing attention on privacy has motivated the rapid design and implementation of multiple privacy-preserving methods. This chapter first presents a few modern approaches for privacy preservation, including anonymization, differential privacy, federated learning, homomorphic encryption, and secure multi-party computation. Then, a privacy-preserving anomaly detection scheme under the E-Health system is presented. In the era of modern communication technologies and big data, E-Health has emerged and become a popular paradigm. However, recent E-Health applications have raised serious concerns when detecting the anomalous behaviors in a person's sensitive medical records. Such records contain private data of health conditions, some of which are maliciously compromised and modified by adversaries. This privacy-preserving anomaly detection scheme is introduced to strengthen the understanding of detecting anomalous behaviors without compromising information confidentiality. This scheme is enabled by predicate encryption. Specifically, the novelty is the use of a session key as a message payload during the encryption operation of the predicate encryption system in order to achieve both information privacy and efficient cryptographic computations. Moreover, the system model and security model are defined. Detailed descriptions on the design of predicates and anomaly detection procedures are presented. Lastly, evaluations are performed in terms of computational overhead, communication overhead, and anomaly detection." @default.
- W4308191331 created "2022-11-09" @default.
- W4308191331 date "2022-11-04" @default.
- W4308191331 modified "2023-09-26" @default.
- W4308191331 title "Privacy Preservation in the Era of Big Data" @default.
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- W4308191331 doi "https://doi.org/10.1002/9781119784135.ch7" @default.
- W4308191331 hasPublicationYear "2022" @default.
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