PROTECTED AND DYNAMIC KEYWORD SEARCH RANK SCHEME FOR CLOUD DATABASE

The major aim of this paper is to solve the problem of multi-keyword ranked search over encrypted cloud data (MRSE) at the time of protecting exact method wise privacy in the cloud computing concept. Data holders are encouraged to outsource their difficult data management systems from local sites to the business public cloud for large flexibility and financial savings. However for protecting data privacy, sensitive data have to be encrypted before outsourcing, which performs traditional data utilization based on plaintext keyword search. As a result, allowing an encrypted cloud data search service is of supreme significance. In view of the large number of data users and documents in the cloud, it is essential to permit several keywords in the search demand and return documents in the order of their appropriate to these keywords. Similar mechanism on searchable encryption makes centre on single keyword search or Boolean keyword search, and rarely sort the search results. In the middle of various multi-keyword semantics, deciding the well-organized similarity measure of “coordinate matching,” it means that as many matches as possible, to capture the appropriate data documents to the search query. Particularly, we consider “inner product similarity” i.e., the amount of query keywords shows in a document, to quantitatively estimate such match measure that document to the search query. Through the index construction, every document is connected with a binary vector as a sub index where each bit characterize whether matching keyword is contained in the document. The search query is also illustrates as a binary vector where each bit means whether corresponding keyword appears in this search request, so the matched one could be exactly measured by the inner product of the query vector with the data vector. On the other hand, directly outsourcing the data vector or the query vector will break the index privacy or the search privacy. The vector space model facilitate to offer enough search accuracy, and the DES encryption allow users to occupy in the ranking while the popularity of computing work is done on the server side by process only on cipher text. As a consequence, data leakage can be eradicated and data security is guaranteed

A practical and secure multi-keyword search method over encrypted cloud data

Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective

Efficient and secure ranked multi-keyword search on encrypted cloud data

Information search and document retrieval from a remote database (e.g. cloud server) requires submitting the search terms to the database holder. However, the search terms may contain sensitive information that must be kept secret from the database holder. Moreover, the privacy concerns apply to the relevant documents retrieved by the user in the later stage since they may also contain sensitive data and reveal information about sensitive search terms. A related protocol, Private Information Retrieval (PIR), provides useful cryptographic tools to hide the queried search terms and the data retrieved from the database while returning most relevant documents to the user. In this paper, we propose a practical privacy-preserving ranked keyword search scheme based on PIR that allows multi-keyword queries with ranking capability. The proposed scheme increases the security of the keyword search scheme while still satisfying efficient computation and communication requirements. To the best of our knowledge the majority of previous works are not efficient for assumed scenario where documents are large files. Our scheme outperforms the most efficient proposals in literature in terms of time complexity by several orders of magnitude

A Compression Technique Exploiting References for Data Synchronization Services

Department of Computer Science and EngineeringIn a variety of network applications, there exists significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.4%, 20.1%, 29.9%, and 61.2%, and are about 78.3%, 79.6%, 86.1%, and 92.9% in a web browsing scenario, respectively.ope