Biblio

Cost-effective and efficient sequencing technologies have resulted in massive genomic data availability. To compute on a large-scale genomic dataset, it is often required to outsource the dataset to the cloud. To protect data confidentiality, data owners encrypt sensitive data before outsourcing. Outsourcing enhances data owners to eliminate the storage management problem. Since genome data is large in volume, secure execution of researchers query is challenging. In this paper, we propose a method to securely perform count query on datasets containing genotype, phenotype, and numeric data. Our method modifies the prefix-tree proposed by Hasan et al. [1] to incorporate numerical data. The proposed method guarantees data privacy, output privacy, and query privacy. We preserve the security through encryption and garbled circuits. For a query of 100 single-nucleotide polymorphism (SNPs) sequence, we achieve query execution time approximately 3.5 minutes in a database of 1500 records. To the best of our knowledge, this is the first proposed secure framework that addresses heterogeneous biomedical data including numeric attributes.

Problems related to privacy and cyber-attacks have increased in recent years as a result of the rapid development of cloud computing. This work concerns secure cloud computing services on a blockchain platform, called cloud@blockchain, which benefit from the anonymity and immutability of blockchain. Two functions- anonymous file sharing and inspections to find illegally uploaded files- on cloud@blockchain are designed. On cloud@blockchain, cloud users can access data through smart contracts, and recognize all users within the application layer. The performance of three architectures- a pure blockchain, a hybrid blockchain with cache and a traditional database in accessing data is analyzed. The results reveal the superiority of the hybrid blockchain with the cache over the pure blockchain and the traditional database, which it outperforms by 500% and 53.19%, respectively.

The reverse k-nearest neighbours search is a fundamental primitive in multi-dimensional (i.e. multi-attribute) databases with applications in location-based services, online recommendations, statistical classification, pat-tern recognition, graph algorithms, computer games development, and so on. Despite the relevance and popularity of the query, no solution has yet been put forward that supports it in encrypted databases while protecting at the same time the privacy of both the data and the queries. With the outsourcing of massive datasets in the cloud, it has become urgent to find ways of ensuring the fast and secure processing of this query in untrustworthy cloud computing. This paper presents searchable encryption schemes which can efficiently and securely enable the processing of the reverse k-nearest neighbours query over encrypted multi-dimensional data, including index-based search schemes which can carry out fast query response that preserves data confidentiality and query privacy. The proposed schemes resist practical attacks operating on the basis of powerful background knowledge and their efficiency is confirmed by a theoretical analysis and extensive simulation experiments.