Efficient Multi - Keyword Ranked Search over Encrypted Cloud Computing

Cloud computing allow customer to store their data on remote site so it reduce burden on local complex data storing. But before storing sensitive data it can encrypted and this can overcome plaintext keyword search.AS large number of user and data on cloud and for search on that data allow multi keyword search also provide result similarity ranking for effective retrieval of data. From number of multi-keyword semantics to identify similarity between search query and data highly efficient rule of coordinate matching, i.e., as many matches as possible, and then use inner data similarity for quantitatively similarity measure. In this system, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE),and establish a set of strict privacy requirements for such a secure cloud data utilization system to be implemented in real. We first propose basic idea of different privacy preserving multi-keyword search technique along with search on data that store on cloud in encrypted form and maintaining the integrity of rank order in search result and the cloud server is untrusted. .By hiding the user’s identity, the confidentiality of user’s data is maintaine

Multi Keyword Similarity Search Over Encrypted Text Data on Cloud

The tremendous amount of data is being outsourced every day by individuals or enterprises . It is not feasible to manage or to store such a large data locally, due to the limited storage capacities, and the system becomes the single point of failure. the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive, the data which user wants to store on the cloud can be private and it should not be leaked, for that purpose searchable encryption is be used, so that even if the file falls in wrong hands it will be safe. At the time of retrieval of data, the multi-keyword search over text data can only handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Inverted List is used to implement searching on index

Survey Paper on Multi Keyword Similarity Search over Encrypted Cloud Data

The tremendous amount of data outsourced every day by individuals or each enterprises . It is impossible to manage or to store this complex data at individual level, as the chances of crash the system is more, and the system becomes the single point of failure.When we feel the need of storing the data in such a way that it can be accessed uninterruptedly, then there the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive. Considering the privacy of the data over the cloud, for that searchable encryption can be used. At the time of retrieval of data, consider the multi-keyword search over outsourced cloud text data only as it can handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Searchable encryption also enables searching without revealing any additional information. Using multi-keyword similarity search cloud returns the files containing more number of matches with user input keywords and similar keyworks. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Filter’s bit pattern is used to speedup and it is efficient in terms of the search time at the cloud side. This paper presents a review on various existing Similarity searching techniques

Secure Searching Mechanism for Cloud Computing Based Cloud Storage System

Cloud computing has been considered an enterprise for IT infrastructure, which can organize huge resource of computing, storage and applications, and enable users to enjoy ubiquitous, convenient on-demand network access to a configurable computing resources with great efficiency and minimal economic overhead for shared pool. Attracted by these appealing features, both individuals and enterprises are motivated to contract out their data to the cloud, instead of purchasing software and hardware to manage the data themselves. So far, most of the works have been proposed under different threat models to achieve various search functions, such as single keyword search, similarity search, multi- keyword Boolean search, ranked search, multi-keyword ranked search, etc. Among them, multikeyword ranked search achieves more attention for its practical applicability. propose a secure and ranked multikeyword search protocol in a multi-owner cloud model over encrypted cloud data

Efficient Multi-User Keyword Search over Encrypted Data in Cloud Computing

As cloud computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, we propose a new method to enable effective fuzzy keyword search in a multi-user system over encrypted cloud data while maintaining keyword privacy. In this new system, differential searching privileges are supported, which is achieved with the technique of attribute-based encryption. Edit distance is utilized to quantify keywords similarity and develop fuzzy keyword search technique, which achieve optimized storage and representation overheads. We further propose a symbol-based trie-traverse searching scheme to improve the search efficiency. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search with multiple users

Multi-keyword Ranked Search over Encrypted Cloud Data Using RSA Algorithm

Ever since Cloud computing introduced, 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 have 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 data in cloud computing (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. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication

Enabling Efficient Fuzzy Keyword Search over Encrypted Data in Cloud Computing

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only \emph{exact} keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users\u27 searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop two advanced techniques on constructing fuzzy keyword sets, which achieve optimized storage and representation overheads. We further propose a brand new symbol-based trie-traverse searching scheme, where a multi-way tree structure is built up using symbols transformed from the resulted fuzzy keyword sets. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution

Secure and Efficient Utilization of Encrypted Cloud Data using Multi-Keyword Ranked Search

Cloud Computing is a technology that provides services to users such as software as a service, platform as a service and storage as a service. These services are provided based on Pay-per-Use basis so these services are cost effective and flexible. Due to this advantage of cloud computing, the individuals as well as the enterprises are getting motivated to shift their local sensitive and huge data management system to cloud storage. But the sensitive data has to be encrypted before outsourcing in order to provide security to the data. After the data has outsourced it has to be utilized efficiently without losing the originality as it was stored. In this paper we provide a mechanism called ”Multi-keyword Ranked Search over Encrypted cloud data” that gives better and efficient searched result over the encrypted data taking multiple keywords as query, which obsoletes the tradition searching scheme based on plain text search. And we use a “Coordinate Matching” technique to find as many matches as possible and use “inner product similarity” to retrieve relevance search results. So if user wants to retrieve the data stored on cloud, he can specify the multiple keywords and rank for relevance retrieval of results. Finally results the user with top ranked files. DOI: 10.17762/ijritcc2321-8169.160415

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

Ranking based search in the encrypted cloud environment

Cloud computing is emerging as a promising technology for outsourcing of data and quality of data services. However, information which is sensitive when upload on cloud eventually cause privacy problems. Data encryption provides security of data to some level, but at the cost of compromised efficiency. This paper focus on addressing data privacy problems. For the first time, the privacy issue is formulated from the aspect of similarity relevance of data and scheme robustness. Privacy of data is not assured if Server-side ranking based on order-preserving encryption is maintained. For the assurance of data privacy, multi-keyword ranked search over encrypted data in cloud computing (MRSE) scheme is proposed which supports top-k multi keyword retrieval. In MRSE, vector space model and Homomorphic encryption were employed. The vector space model helps to provide accuracy sufficient search of data and the Homomorphic encryption enables users to involve in the encryption of data. The majority of computing work is done on the server side. As a result, leakage of information can be eliminated and data security is ensured. DOI: 10.17762/ijritcc2321-8169.15028