Cloud computing is a technology that provides users with a large storage space and an enormous computing power. However, the outsourced data are often sensitive and confidential, and hence must be encrypted before being outsourced. Consequently, classical search approaches have become obsolete and new approaches that are compatible with encrypted data have become a necessity. For privacy reasons, most of these approaches are based on the vector model which is a time consuming process since the entire index must be loaded and exploited during the search process given that the query vector must be compared with each document vector. To solve this problem, we propose a new method for constructing a secure inverted index using two key techniques, homomorphic encryption and the dummy documents technique. However, 1) homomorphic encryption generates very large ciphertexts which are thousands of times larger than their corresponding plaintexts, and 2) the dummy documents technique that enhances the index security produces lots of false positives in the search results. The proposed approach exploits the advantages of these two techniques by proposing two methods called the compressed table of encrypted scores and the double score formula. Moreover, we exploit a second secure inverted index in order to manage the users’ access rights to the data. Finally, in order to validate our approach, we performed an experimental study using a data collection of one million documents. The experiments show that our approach is many times faster than any other approach based on the vector model.

Secure Inverted Index Based Search over Encrypted Cloud Data with User Access Rights Management

Companies and other organizations such as hospitals seek more and more to enjoy the benefits of cloud computing in terms of storage space and computing power. However, outsourced data must be encrypted in order to be protected against possible attacks. Therefore, traditional information retrieval systems (IRS) are no longer effective and must be adapted in order to work over encrypted cloud data. In addition, in order to provide the ability to search over an encrypted index, we use the vector model to represent documents and queries which is the most used in the literature. During the search process, the query vector must be compared with each document vector which is a time consuming process since the data collection is generally huge. Consequently, the search performance is degraded and the search process is too slow. To overcome this drawback, we propose the use of High Performance Computing (HPC) architectures to accelerate the search over encrypted cloud data. Indeed, we propose several techniques that take benefit from Graphics Processing Unit (GPU) and computer cluster architectures by distributing the work between different threads. In addition, in order to get the best performance, we design our solutions so that they can process several queries simultaneously. The experimental study using 400.000 documents demonstrates the efficiency of our proposals by reaching a speed-up around 46×.

Accelerated search over encrypted cloud data

Cloud computing is a technology that allows companies and individuals to outsource their data and their applications. The aim is to take advantage from the power of storage and processing offered by such technology. However, in order to preserve data privacy, it is crucial that all data must be encrypted before being outsourced into the cloud. Moreover, authorized users should be able to recover their outsourced data. This process can be complicated due to the fact that data are encrypted. The traditional information retrieval systems only work over data in the clear. Therefore, dedicated information retrieval systems were developed to deal with the encrypted cloud data. Several kinds of search over cloud data have been proposed in the literature such as Boolean search, multi-keyword ranked search and fuzzy search. However, the semantic search is little addressed in the literature. In this paper, we propose an approach called SSE-S that take into account the semantic search in the cloud by using Wikipedia ontology to understand the meaning of documents and queries with maintaining the security and the privacy issues.

Concept-based Semantic Search over Encrypted Cloud Data

Cloud computing is a technology that provides users with a large storage space and an enormous computing power. For privacy purpose, the sensitive data should be encrypted before being outsourced to the cloud. To search over the outsourced data, searchable encryption (SE) schemes have been proposed in the literature. An SE scheme should perform searches over encrypted data without causing any sensitive information leakage. To this end, a few security constraints were elaborated to guarantee the security of the SE schemes, namely, the keyword privacy, the trapdoor unlinkability, and the access pattern. The latter is very hard to be respected and most approaches fail to guarantee the access pattern constraint when performing a search. This constraint consists in hiding from the server the search result returned to the user. The non respect of this constraint may cause sensitive information leakage as demonstrated in the literature. To fix this security lack, we propose a method that allows to securely request and receive the needed documents from the server after performing a search. The proposed method that we call the access pattern hiding (APH) technique allows to respect the access pattern constraint. An experimental study is conducted to validate the APH technique.

Access Pattern Hiding in Searchable Encryption

Cloud computing is becoming more and more popular recently, and this trend encourages large corporations and companies to outsource their data to a remote cloud server. However, the concern of data privacy and security is a huge problem which impedes the development of cloud computing. It is essential and necessary to propose a scheme to realize the information retrieval and protect the data privacy at the same time. Since ranked multi-keyword search is a pretty practical question in this area and it has a significant influence on the user experience, we would mainly focus on the scheme to achieve high performance and low cost multi-keyword ranked search. In this paper, we proposed a novel search scheme with Johnson-Lindenstrauss transform (J-L scheme for short) and a technique called "optimised maximum query" to live up to our efficiency demand. Furthermore, we established a complete system to guarantee a set of strict privacy requirements. Extensive experiments on real-world database are presented to show our proposed scheme's efficiency and accuracy.

http://or.nsfc.gov.cn/bitstream/00001903-5/419712/1/1000007116161.pdf

Rundong Liu

Papers

An Efficient Multi-keyword Ranked Retrieval Scheme with Johnson-Lindenstrauss Transform over Encrypted Cloud Data