Privacy- Preserving Keyword-based Semantic Search over Encrypted Cloud Data

Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, the potential of cloud services has not been fully realized due to users concerns about data privacy and security. User-side encryption techniques can be employed to mitigate the security concerns, but once the data is encrypted, no processing (eg, searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are encrypted. These techniques enable various types of search on the encrypted data and offer different levels of security. While these techniques enable different search types and vary in details, they share similarities in their components and architectures. In this paper, we provide a comprehensive survey on different secure search techniques, a high-level architecture for these systems, and an analysis of their performance and security level. KEYWORDScloud security, encrypted search, search over encrypted data, survey INTRODUCTIONAs cloud computing becomes prevalent, more cloud-based solutions are being developed and widely used in different applications. Companies that have adopted cloud storage solutions are reported to gain a competitive edge against those that have not. 1Cloud computing is favored due to its many advantages, including convenience and accessibility, consistent back ups to reducing the burden of local storage, and saving capital expenditure on in-house hardware and software maintenance. 2 However, public cloud storage services may be utilized by multi-tenant customers who store large amounts of potentially sensitive data on the cloud. Using cloud storage implies losing full control over data and delegating it to the cloud administrators, exposing the data to potential external and internal attacks, 3,4 which can be devastating for organizations that rely on confidentiality of their data (eg, financial corporations).These problems have made businesses concerned about outsourcing their data to the cloud and utilizing its potential. 5,6 For instance, a medical center that owns patients' health records cannot outsource its data to a cloud that is vulnerable to attacks, due to legal regulations. 7 In another instance, a law enforcement agency that keeps sensitive criminal records will hesitate to use cloud storage because of similar concerns.this approach remains impractical. Therefore, searchable encryption systems (eg, see other works 12-15 ) have been introduced to cope with this problem. These systems ideally allow the encrypted data to be searched without revealing the data and search query. Hence, they relieve concerns about data confidentiality in the cloud. Efforts to create searchable encryption systems date back to 2000 with work by Song et al. 11 Since then, numerous research works have been undertaken to enable different types of searchable encryption. Although these systems differ in their search approaches, security level, and perf...

Section: Elements Of a Cloud-based Searchable Encryption Systemmentioning

confidence: 99%

Section: Setup Mechanismmentioning

confidence: 99%

“…14,15,21,35 The Index structure is also known as ''meta data'' 18 or ''identify keywords.'' 14,15,21,35 The Index structure is also known as ''meta data'' 18 or ''identify keywords.''…”

Section: Setup Mechanismmentioning

confidence: 99%

Section: Ontological Semantic Searchmentioning

confidence: 99%

Section: Ontological Semantic Searchmentioning

confidence: 99%

See 3 more Smart Citations

Survey on secure search over encrypted data on the cloud

Pham

Woodworth

Salehi

2019

S3BD: Secure semantic search over encrypted big data in the cloud

Woodworth

Salehi²

2018

Summary Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client‐end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real‐time searching over the encrypted big data in the cloud. In particular, semantic search is of interest to clients dealing with big data. To address this problem, in this research, we develop a system (termed S3BD) for searching big data using cloud services without exposing any data to cloud providers. To keep real‐time response on big data, S3BD proactively prunes the search space to a subset of the whole dataset. For that purpose, we propose a method to cluster the encrypted data. An abstract of each cluster is maintained on the client‐end to navigate the search operation to appropriate clusters at the search time. Results of experiments, carried out on real‐world big datasets, demonstrate that the search operation can be achieved in real‐time and is significantly more efficient than other counterparts. In addition, a fully functional prototype of S3BD is made publicly available.

Document clustering for efficient and secure information retrieval from cloud

Handa

Krishna

Aggarwal

2019

Summary Organizations prefer using cloud for storing their data due to availability of cost‐effective storage. The outsourced data include sensitive information, so data is encrypted as maintaining confidentiality and privacy of the documents is of paramount importance. Retrieving the desired information from the cloud requires efficient searching, which involves submission of search query to the cloud server by the end‐user. As the search terms may include sensitive information of an organization, it is desired that the search query should not reveal any confidential information. The existing works are not suitable for big‐data scenario due to high search time required for large document collections, thereby leading to increased cloud usage cost. Thus, an efficient approach to perform search on encrypted data using clustering is proposed in this paper. As the proposed technique clusters the documents based on the relationship between the keywords, the search method involves searching documents within the relevant cluster in contrast to searching the entire dataset. An efficient ranking method is incorporated to rank the documents according to the relevance to search query using Term Frequency‐Inverse Document Frequency (tf‐idf) value of the keywords in the documents, which leads to reduced communication overheads due to reduction in unnecessary documents being downloaded. Moreover, an efficient query randomization approach is proposed so that two or more queries involving the same search terms appear distinct. Experimental results using real datasets demonstrate that our proposed multi‐keyword ranked search scheme on encrypted cloud data significantly reduce the number of comparisons and search time in comparison to the existing techniques while maintaining recall of 100% and precision of 82%.