2018
DOI: 10.1186/s42400-018-0005-8
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Ensuring data confidentiality via plausibly deniable encryption and secure deletion – a survey

Abstract: Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger data owners' privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA and Sarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved in the personal computing devices (i.e., confidentiality during their lifetime); and also, they should be rendered irrecoverable after they are removed from the devices (i.e., c… Show more

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Cited by 24 publications
(7 citation statements)
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“…In summary of this section, encryption techniques like Homomorphic Encryption, Differential Privacy, and SMPC play a vital role in modern data security. They provide a secure and robust way to protect sensitive information, ensure privacy, and enable secure computations on encrypted data ( Cossy-Gantner et al, 2018 ; Zhang et al, 2018 ).…”
Section: Ethical Considerations In Ai Decision-makingmentioning
confidence: 99%
“…In summary of this section, encryption techniques like Homomorphic Encryption, Differential Privacy, and SMPC play a vital role in modern data security. They provide a secure and robust way to protect sensitive information, ensure privacy, and enable secure computations on encrypted data ( Cossy-Gantner et al, 2018 ; Zhang et al, 2018 ).…”
Section: Ethical Considerations In Ai Decision-makingmentioning
confidence: 99%
“…In summary of this section, encryption techniques like Homomorphic Encryption, Differential Privacy, and SMPC play a vital role in modern data security. They provide a secure and robust way to protect sensitive information, ensure privacy, and enable secure computations on encrypted data [24].…”
Section: How Can Algorithms Help To Protect Our Privacymentioning
confidence: 99%
“…Then, Ginart et al [8] formalized the problem of efficient data forgetting and provided engineering principles for designing forgetting algorithms. However, these methods only behave well for nonadaptive (later training does not depend on earlier training) machine learning models, such as kmeans clustering [9]. For that, Bourtoule et al [10] proposed SISA (sharded, isolated, sliced, and aggregated), a model-independent method, which divides the training set into disjoint slices.…”
Section: Introductionmentioning
confidence: 99%