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Authorized Efficient Similarity for Secure and Efficient Keyword Search over Outsourced Cloud Computing
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rohini, M. Behra, S. N. Ramakrishna, Ch. |
| Copyright Year | 2013 |
| Abstract | In cloud computing, clients usually outsource their data to the cloud storage servers to reduce the management costs. Although cloud based services offer many advantages,nprivacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. This paper introduces a novel approach in the field of encrypted searching that allows both encrypted phrase searches and proximity ranked multi-keyword searches to encrypted datasets on un-trusted cloud. While those data may contain sensitive personal information, he cloud servers cannot be fully trusted in protecting them. Encryption is a promising way to protect the confidentiality of the outsourced data, but it also introduces much difficulty to performing effective searches over encrypted information. 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. we are able to allow for a full range of search features in our encrypted searches, something that has never been accomplished before. Furthermore, our approach permits the encrypted corpus and index to both be stored on cloud data servers. we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-theart 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. we enhance the query privacy which hides users’ query keywords against the server. We implement our scheme on a modern workstation, and experimental results demonstrate its suitability for practical usage. I.Introduction Cloud computing becomes prevalent due to the fact that it removes the burden of large scale data management in a cost effective manner. Hence, huge amount of data, ranging from personal health records to e-mails, are increasingly outsourced into the cloud. At the same time, transfer of sensitive data to untrusted cloud servers leads to concerns about its privacy. Encryption is a method that secures information by making it illegible or indistinguishable from random noise to anyone that does not have some privileged information, a key. The practice of using cryptography to encrypt sensitive information has been around for millennia. For thousands of years a major tenet was that the encrypted information was unusable until decrypted. This served well until recent, when the vast number of documents needing to be encrypted has made decrypting individual documents to find query results infeasible in practice. Searchable encryption was invented to solve the problem of how to find keywords in documents that are encrypted without decrypting the entire corpus set. To mitigate the concerns, sensitive data is usually outsourced in encrypted form which prevents unauthorized access. Although encryption provides protection, it significantly complicates the computation on the data such as the fundamental search operation. Still, cloud services should enable efficient search on the encrypted data to ensure the benefits of a fullfledged cloud computing environment. In fact, sizable amount of algorithms have been proposed to support the task which are called searchable encryption schemes [1]–[8]. Traditionally, almost all such schemes have been designed for exact query matching. They enable selective retrieval of the data from the cloud according to the existence of a specified feature. Despite enthusiasm around the cloud data service outsourcing model, its promises cannot be fulfilled until we address the serious security and privacy concerns that data owners have. The outsourced data may contain very ROHINI --International Journal of Computer Science information and Engg., Technologies ISSN 2277-4408 || 01092013-012 IJCSIET-ISSUE3-VOLUME3-SERIES1 Page 2 sensitive information, such as Personal Health Records (PHRs), face-book photos, and business documents. Many people remain dubious about the levels of privacy protection of their data when stored in a server owned by a third-party cloud service provider. Given that there have been numerous reported data breaches related to cloud servers [2], which could be due to malicious attacks, theft or internal software bugs and errors, it can be claimed that the servers are not fully trustworthy. This implies that there is no absolute governance about how the owners’ information will be used and whether the owners actually control access to their data. To cope with the tough trust issues and to ensure owners’ control over their own privacy, applying data encryption on the documents before outsourcing has been proposed as a promising solution, which is already adopted by many recent works [22], [7], [24]. In this paper, we focus on the “multi-owner” setting, where the encrypted data are contributed by multiple owners and can be searched by multiple users. A similarity search problem consists of a collection of data items that are characterized by some features, a query that specifies a value for a particular feature and a similarity metric to measure the relevance between the query and the data items. The goal is to retrieve the items whose similarity against the specified query is greater than a predetermined threshold under the utilized metric. Although exact matching based searchable encryption methods are not suitable to achieve this goal, there are some sophisticated cryptographic techniques that enable similarity search over encrypted data [9], [10]. Unfortunately, such secure multi-party computation based techniques incur substantial computational resources. According to a recent survey [11], secure edit distance computations [10] require over two years to compute similarity between two datasets of 1000 strings each, on a commodity server. It is apparent that we need efficient methods to perform similarity search over large amount of encrypted data. In this paper, we propose a secure index based encryption scheme to meet this requirement. The basic building block of our secure index is the state-oftheart approximate near neighbor search algorithm in high dimensional spaces called locality sensitive hashing (LSH) [12]. LSH is extensively used for fast similarity search on plain data in information retrieval community (e.g., [13]). In our scheme, we propose to utilize it in the context of the encrypted data. In such a context, it is critical to provide rigorous security analysis of the scheme to ensure the confidentiality of the sensitive data. In fact, we provide a strong security definition and prove the security of the proposed scheme under the provided definition. Secure LSH Index : To utilize the appealing properties of LSH in the context of the encrypted data, we propose a secure LSH index and a similarity searchable symmetric encryption scheme on top of this index. In addition, we adapt the widely accepted adaptive semantic security definition of Curtmola et. al. [4] for searchable symmetric encryption schemes and prove the security of the proposed scheme under the adapted definition. Fault Tolerant Keyword Search : We provide an important application of the proposed scheme for fault tolerant keyword search over encrypted data. Typographical errors are common oth in the search queries and the data sources, but most of the available searchable encryption schemes do not tolerate such errors. Recently, a fuzzy keyword set based scheme has been proposed to handle the problem [5]. Although the approach of [5] provides a solution to some extent, it is specific to a particular distance measure. On the other hand, our scheme provides more generic solution and it can be utilized for distinct similarity search contexts where LSH is applicable. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijcsiet.com/pdf/01092013-012.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
