Earticle

Nowadays, more and more people are motivated to outsource their local data to public cloud servers for great convenience and reduced costs in data management. But in consideration of privacy issues, sensitive data should be encrypted before outsourcing, which obsoletes traditional data utilization like keyword-based document retrieval. In this paper, we present a secure and efficient multi-keyword ranked search scheme over encrypted data, which additionally supports dynamic update operations like deletion and insertion of documents. Specifically, we construct an index tree based on vector space model to provide multi-keyword search, which meanwhile supports flexible update operations. Besides, cosine similarity measure is utilized to support accurate ranking for search result. To improve search efficiency, we further propose a search algorithm based on “Greedy Depth-first Traverse Strategy”. Moreover, to protect the search privacy, we propose a secure scheme to meet various privacy requirements in the known ciphertext threat model. Experiments on the real-word dataset show the effectiveness and efficiency of proposed scheme.

With the emergence of cloud computing, many data owners outsource their local data to cloud server so as to enjoy high-quality data storage services. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although existing searchable encryption technologies enable data users to conduct secure search over encrypted data, the functionality of these schemes need to be further improved. In this paper, we construct a secure and efficient multi-keyword ranked search scheme which supports both the semantic extension search and the multi-keyword ranked search. The semantic extension is achieved through the mutual information statistical analysis of keywords. And the multi-keyword ranked search is achieved through a balanced binary tree whose nodes are the vectors of term frequency (TF) values. The splitting operation and secure transformation are utilized to encrypt the vectors of index and query. Note that, the encrypted vectors can be well used to calculate accurate relevance scores. Phantom terms are added to the index vector to blind the search results to resist statistical attacks. Due to the use of tree-based index structure, the proposed scheme can achieve the sub-linear search time. Finally, the experiments are conducted to demonstrate the efficiency of the proposed scheme.

With the development of cloud computing technology, more and more users have outsourced their data to cloud platform. To protect the safety of these data, some encryption methods are usually used. But it is very difficult question that how to effective retrieval and to use the encrypted data, we propose a multi-attribute keyword retrieval mechanism for encrypted cloud data. In this mechanism, we first define the local feature attributes of the keywords and the global feature attributes of the document. Then, we construct the mapping relationship between keywords and document attributes according to the basic principle of inverted index algorithm and establish the security index. Based on the above steps, we improve the weight of the key words and calculate the similarity score of the document by combining the multi-attribute score function. Experiments show that this mechanism not only can effectively improve the accuracy of the data retrieval, but also can greatly reduce the bandwidth consumption of system.

With the development of cloud computing, the sensitive information of outsourced data is at risk of unauthorized accesses. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. Hence, it is an especially important thing to explore secure encrypted cloud data search service. Considering the huge number of outsourced data, there are three problems we are focused on to enable efficient search service: multi-keyword search, result relevance ranking and dynamic update. In this paper, we propose a practically efficient and flexible searchable encrypted scheme which supports both multi-keyword ranked search and dynamic update. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search result. To improve search efficiency, we design a tree-based index structure which supports insertion and deletion update well without privacy leakage. We propose a secure search scheme to meet the privacy requirements in the threat model. Finally, experiments on real-world dataset are implemented to demonstrate the overall performance of the proposed scheme, which show our scheme is efficient.

To protect the privacy, sensitive information has to be encrypted before outsourcing to the cloud. Thus the effective data utilization becomes a significant challenge. Searchable encryption scheme has been developed to conduct retrieval over encrypted data. However, these schemes only support exact keyword search. Recent fuzzy search schemes mainly evaluate the similarity of keywords from the structure but the semantic relatedness is not considered. Our work focuses on realizing secure semantic search through query keyword semantic extension. Based on the co-occurrence probability of terms, the semantic relationship library is constructed to record the semantic similarity between keywords. We exploited architecture of two clouds, namely private cloud and public cloud. The search operation is divided into two steps. The first step expands the query keyword upon SRL stored in the private cloud. The second step uses the extended query keywords set to retrieve the index on public cloud. Finally the matched files are returned in order. Detailed security analysis shows that our solution is privacy-preserving and secure. Experimental evaluation demonstrates the efficiency and effectives of the scheme.

There is a shortcoming in reversible data-hiding algorithm in encrypted images, it is the low capacity. we put forward a new algorithm oriented security application in cloud computing, in the new algorithm of reversible data-hiding algorithm in encrypted image, we bring in cloud computing service and singular value decomposition based on matrix. Using the powerful computing and storage capacity, compute the singular value decomposition of some selected bit planes of the encrypted image, and store the recovery dictionary that generated in this process, in the end ,we directly embed the information into the singular value matrix. Experimental results show that under the premise of ensured image privacy security and higher image fidelity, the embedding capacity has been significantly improved, and the image can be fully recovered after information extraction. In the same time, the processes of information extraction and image restoration are separable. Compared with other algorithms, it has higher embedding capacity.

With growing popularity of cloud computing services, users can more easily manage massive amount of data by outsourcing them to the cloud, or more efficiently analyse large amount of data by leveraging IT infrastructure provided by the cloud. This, however, brings the security concerns of sensitive data. To provide data security, it is essential to encrypt sensitive data before uploading it to cloud computing services. Although data encryption helps provide data security, it negatively affects the performance of massive data analytics because it forbids the use of index and mathematical operation on encrypted data. Thus, in this paper, we propose a novel algorithm which enables to efficiently process a large amount of encrypted data. In particular, we propose a novel top-k processing algorithm on the massive amount of encrypted data in the cloud computing environments, and verify the performance of the proposed approach with real data experiments.

최근 다양한 분야에서 생산되는 데이터의 양이 폭발적으로 증가함에 따라 사용자가 가장 관심 있어 하는 몇 개의 데이터를 검색하는 top-k 질의에 대한 관심이 고조되고 있다. Top-k 질의는 사용자의 점수 함수를 이용하여, 사용자가 원하는 모든 조건을 만족시키는 데이터들 중에서 최상위 (또는 최하위) 점수를 가지는 k개의 데이터를 사용자에게 반환한다. 최근 들어 클라우드 컴퓨팅 서비스의 대중화로 인하여 사용자의 대용량 데이터를 클라우드에 아웃소싱하여 경제적으로 저장 및 관리하는 데이터 아웃소싱이 크게 주목받고 있다. 그러나 데이터 아웃소싱으로 인하여 사용자의 민감한 데이터가 클라우드 서비스 제공자에게 노출될 수 있다는 위험이 존재하며, 이러한 문제를 방지하기 위해서는 사용자의 민감한 데이터를 암호화하여 클라우드에 저장하는 것이 필수적으로 요구된다. 본 논문은 클라우드 컴퓨팅 환경에서 암호화된 데이터에 대한 top-k 질의를 효율적으로 처리하는 알고리즘을 제안한다. 제안되는 알고리즘은 순서보존 암호화 기법을 이용하여, 암호화된 데이터만을 대상으로 top-k 질의 결과에 포함되지 않을 것으로 예상되는 중간 결과들을 클라우드 내에서 미리 제거함으로써 효율적인 top-k 질의 처리가 가능하게 한다. 논문의 실험 결과는 제안된 top-k 질의 처리 알고리즘이 단순 방법과 비교하여 사용자 시스템의 부하를 10배~10000배 줄일 수 있음을 증명한다.

Recently top-k query processing has been extremely important along with the explosion of data produced by a variety of applications. Top-k queries return the best k results ordered by a user-provided monotone scoring function. As cloud computing service has been getting more popular than ever, a hot attention has been paid to cloud-based data outsourcing in which clients' data are stored and managed by the cloud. The cloud-based data outsourcing, though, exposes a critical secuity concern of sensitive data, resulting in the misuse of unauthorized users. Hence it is essential to encrypt sensitive data before outsourcing the data to the cloud. However, there has been little attention to efficient top-k processing on the encrypted cloud data. In this paper we propose a novel top-k processing algorithm that can efficiently process a large amount of encrypted data in the cloud. The main idea of the algorithm is to prune unpromising intermediate results at the early phase without decrypting the encrypted data by leveraging an order-preserving encrypted technique. Experiment results show that the proposed top-k processing algorithm significantly reduces the overhead of client systems from 10X to 10000X.

클라우드 스토리지 환경에서 중복제거 기술은 스토리지의 효율적인 활용을 가능하게 한다. 또한 클라우드 스토리지 서비스 제공자들은 네트워크 대역폭을 절약하기 위해 클라이언트 측 중복제거 기술을 도입하고 있다. 클라우드 스토리지 서비스를 이용하는 사용자들은 민감한 데이터의 기밀성을 보장하기 위해 데이터를 암호화하여 업로드하길 원한다. 그러나 일반적인 암호화 방식은 사용자마다 서로 다른 비밀키를 사용하기 때문에 중복제거와 조화를 이룰 수 없다. 또한 클라이언트 측 중복제거는 태그 값이 전체 데이터를 대신하기 때문에 안전성에 취약할 수 있다. 최근 클라이언트 측 중복제거의 취약점을 보완하기 위해 소유권 증명 기법들이 제안되었지만 여전히 암호데이터 상에서 클라이언트 측 중복제거 기술은 효율성과 안전성에 문제점을 가지고 있다. 본 논문에서는 전수조사 공격에 저항성을 갖고 암호데이터 상에서 소유권 증명을 수행하는 안전하고 실용적인 클라이언트 측 중복제거 기술을 제안한다.

In cloud storage environment, deduplication enables efficient use of the storage. Also, in order to save network bandwidth, cloud storage service provider has introduced client-side deduplication. Cloud storage service users want to upload encrypted data to ensure confidentiality. However, common encryption method cannot be combined with deduplication, because each user uses a different private key. Also, client-side deduplication can be vulnerable to security threats because file tag replaces the entire file. Recently, proof of ownership schemes have suggested to remedy the vulnerabilities of client-side deduplication. Nevertheless, client-side deduplication over encrypted data still causes problems in efficiency and security. In this paper, we propose a secure and practical client-side encrypted data deduplication scheme that has resilience to brute force attack and performs proof of ownership over encrypted data.