A Data Assured Deletion Scheme in Cloud Storage

In order to provide a practicable solution to data confidentiality in cloud storage service,a data assured deletion scheme,which achieves the fine grained access control,hopping and sniffing attacks resistance,data dynamics and deduplication,is proposed.In our scheme,data blocks are encrypted by a two-level encryption approach,in which the control keys are generated from a key derivation tree,encrypted by an All-OrNothing algorithm and then distributed into DHT network after being partitioned by secret sharing.This guarantees that only authorized users can recover the control keys and then decrypt the outsourced data in an ownerspecified data lifetime.Besides confidentiality,data dynamics and deduplication are also achieved separately by adjustment of key derivation tree and convergent encryption.The analysis and experimental results show that our scheme can satisfy its security goal and perform the assured deletion with low cost.

Privacy Protection for Big Data Linking using the Identity Correlation Approach

Privacy protection for big data linking is discussed here in relation to the Central Statistics Office （CSO）, Ireland＇s, big data linking project titled the ＇Structure of Earnings Survey - Administrative Data Project＇ （SESADP）. The result of the project was the creation of datasets and statistical outputs for the years 2011 to 2014 to meet Eurostat＇s annual earnings statistics requirements and the Structure of Earnings Survey （SES） Regulation. Record linking across the Census and various public sector datasets enabled the necessary information to be acquired to meet the Eurostat earnings requirements. However, the risk of statistical disclosure （i.e. identifying an individual on the dataset） is high unless privacy and confidentiality safe-guards are built into the data matching process. This paper looks at the three methods of linking records on big datasets employed on the SESADP, and how to anonymise the data to protect the identity of the individuals, where potentially disclosive variables exist.

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 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.

Novel virtual user scheme to increase data confidentiality against eavesdropping in OCDMA network

We propose a novel technique to increase the confidentiality of an optical code division multiple access （OCDMA） system. A virtual user technique is analyzed and implemented to make an OCDMA system secure. Using this technique, an eavesdropper will never find an isolated authorized user＇s signal. When authorized users and virtual users transmit data synchronously and asynehronously, network security increases by 25% and 37.5%, respectively.

A Policy Integration Method Based on Multilevel Security for Data Integration

Integrating and sharing data from different data sources is one of the trends to make better use of data. However, data integration hampers data confidentiality where each data source has its own access control policy. This paper includes a discussion on the issue about access control across multiple data sources when they arc combined together in the scenario of searching over these data. A method based on multilevel security for data integration is proposed. The proposed method allows the merging of policies and also tackles the issue of policy conflicts between different data sources.