Ensuring data confidentiality via plausibly deniable encryption and secure deletion – a survey

Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger dataowners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA andSarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved inthe personal computing devices (i.e.,confidentiality duringtheirlifetime); and also, they should be rendered irrecoverableafter they are removed from the devices (i.e.,confidentiality after their lifetime). Encryption and secure deletion are usedto ensure data confidentiality during and after their lifetime, respectively.This work aims to perform a thorough literature review on the techniques being used to protect confidentiality of thedata in personal computing devices, including both encryption and secure deletion. Especially for encryption, wemainly focus on the novel plausibly deniable encryption (PDE), which can ensure data confidentiality against both acoercive (i.e., the attacker can coerce the data owner for the decryption key) and a non-coercive attacker

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., confidentiality after their lifetime). Encryption and secure deletion are used to ensure data confidentiality during and after their lifetime, respectively. This work aims to perform a thorough literature review on the techniques being used to protect confidentiality of the data in personal computing devices, including both encryption and secure deletion. Especially for encryption, we mainly focus on the novel plausibly deniable encryption (PDE), which can ensure data confidentiality against both a coercive (i.e., the attacker can coerce the data owner for the decryption key) and a non-coercive attacker

A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients’ health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks