With the many benefits of cloud computing, an entity may want to outsource its data and their related analytics tasks to a cloud. When data are sensitive, it is in the interest of the entity to outsource encrypted data to the cloud; however, this limits the types of operations that can be performed on the cloud side. Especially, evaluating queries over the encrypted data stored on the cloud without the entity performing any computation and without ever decrypting the data become a very challenging problem. In this paper, we propose solutions to conduct range queries over outsourced encrypted data. The existing methods leak valuable information to the cloud which can violate the security guarantee of the underlying encryption schemes. In general, the main security primitive used to evaluate range queries is secure comparison (SC) of encrypted integers. However, we observe that the existing SC protocols are not very efficient. To this end, we first propose a novel SC scheme that takes encrypted integers and outputs encrypted comparison result. We empirically show its practical advantage over the current state-of-the-art. We then utilize the proposed SC scheme to construct two new secure range query protocols. Our protocols protect data confidentiality, privacy of user's query, and also preserve the semantic security of the encrypted data; therefore, they are more secure than the existing protocols. Furthermore, our second protocol is lightweight at the user end, and it can allow an authorized user to use any device with limited storage and computing capability to perform the range queries over outsourced encrypted data.
@cite_29 proposed a new technique for protecting confidentiality as well as access patterns to the data in outsourced environments. Their technique is based on constructing shuffled index structures using B+-trees. In order to hide the access patterns, their method introduces fake searches in conjunction with the actual index value to be searched. We emphasize that their work solves a different problem - mainly how to securely outsource the index and then obliviously search over this data structure. Their technique has a straight-forward application to keyword search over encrypted data since it deals with exact matching. However, at this point, it is not clear how their work can be extended for range queries that require implicit comparison operations to be performed in a secure manner.
@cite_29
2105751742 1720752185 2032127253 2116717571
As the use of external storage and data processing services for storing and managing sensitive data becomes more and more common, there is an increasing need for novel techniques that support not only data confidentiality, but also confidentiality of the accesses that users make on such data. In this paper, we propose a technique for guaranteeing content, access, and pattern confidentiality in the data outsourcing scenario. The proposed technique introduces a shuffle index structure, which adapts traditional B+-trees. We show that our solution exhibits a limited performance cost, thus resulting effectively usable in practice. Researchers have been studying security challenges of database outsourcing for almost a decade. Privacy of outsourced data is one of the main challenges when the “Database As a Service” model is adopted in the service oriented trend of the cloud computing paradigm. This is due to the insecurity of the network environment or even the untrustworthiness of the service providers. This paper proposes a method to preserve privacy of outsourced data based on Shamir’s secret sharing scheme. We split attribute values into several parts and distribute them among untrusted servers. The problem of using secret sharing in data outsourcing scenario is how to search efficiently within the randomly generated pool of shares. In this paper, at first, we customize Shamir’s scheme to have A Searchable Secret Sharing Scheme (AS4) that enables the efficient execution of different kinds of queries over distributed shares. Then, we extend our method for sharing values to A Secure Searchable Secret Sharing Scheme (AS5) to tolerate statistical attacks based on adversary’s knowledge about outsourced data distribution. In AS5 data shares are generated uniformly across a domain to prevent information leakage about the outsourced data. In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset. With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE).We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.