Pattern matching is one of the most fundamental and important paradigms in several application domains such as digital forensics, cyber threat intelligence, or genomic and medical data analysis. While it is a straightforward operation when performed on plaintext data, it becomes a challenging task when the privacy of both the analyzed data and the analysis patterns must be preserved. In this paper, we propose new provably correct, secure, and relatively efficient (compared to similar existing schemes) public and private key based constructions that allow arbitrary pattern matching over encrypted data while protecting both the data to be analyzed and the patterns to be matched. That is, except the pattern provider (resp. the data owner), all other involved parties in the proposed constructions will learn nothing about the patterns to be searched (resp. the data to be inspected). Compared to existing solutions, the constructions we propose has some interesting properties: (1) the size of the ciphertext is linear to the size of plaintext and independent of the sizes and the number of the analysis patterns; (2) the sizes of the issued trapdoors are constant on the size of the data to be analyzed; and (3) the search complexity is linear on the size of the data to be inspected and is constant on the sizes of the analysis patterns. The conducted evaluations show that our constructions drastically improve the performance of the most efficient state of the art solution.

Abstract. Ensuring confidentiality of outsourced data continues to be an area of active research in the field of privacy protection. Almost all existing privacy-preserving approaches to address this problem rely on heavyweight cryptographic techniques with a large computational overhead that makes inefficient on large databases. In this paper, we address this problem by improving on an existing approach based on a combination of fragmentation and encryption. We present a method for optimizing and executing queries over distributed fragments stored in different Cloud storage service providers. We then extend this approach by presenting a Private Information Retrieval (PIR) based query technique to enforce data confidentiality under a collaborative Cloud storage service providers model.

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Abstract. This paper presents an approach allowing for a given security and utility requirements, the selection of a combination of mechanisms and the way it will be applied to enforce them. To achieve this goal, we firstly use an expressive formal language to specify the security and utility properties required by data owners and the security mechanisms that can be used to enforce them. Second, we extend and use a Graphplan-based approach to build a planning graph representing all possible transformations of the system resulting from the application of security mechanisms. Finally, we define a method to search the best security mechanisms execution plan to transform the used system from its initial state to a state in which the security requirements are enforced.

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