A Study on Deduplication Techniques over Encrypted Data

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

Cloud storage provides the great convenience for people to access their data at anytime from any place. Since cloud storage is usually run by the third-party service provider, keyword search over cloud data with privacy protection is of great importance. Many studies in the literature have proposed keyword search scheme for document search, but, in most schemes, the query keywords must exactly match those in the document indexes. However, it is impractical to restrict query keywords provided by the user when performing the search. This paper proposes the scheme for semantic multi-keyword search over encrypted cloud data. Users are able to select query keywords on their own choice. In addition, the query privacy of the user and the security of the documents are protected simultaneously through encrypted document search to prevent snooping from the cloud service provider. Experiments are conducted using a dataset of massive real world papers. The results show that the proposed scheme can effectively perform the semantic multi-keyword search over encrypted cloud data with great efficiency.

SummaryThe intersection operation on multisets has many applications in different scenarios, such as data mining and pattern matching. This motivates us to study the problem that when users outsource their private encrypted sets and delegate the set‐intersection operation on the corresponding plaintexts to the cloud, the cloud can manage to conduct the operation and make the result verifiable without the decryption capability. We formally introduced a model of Verifiable Intersection Operations on Encrypted Data in Cloud and presented the definitions of the correctness and security properties. We also proposed a concrete scheme basing on the bilinear‐map accumulator and re‐encryption. The scheme was proved secure under some cryptographic assumptions. And the experimental results of the algorithm implementation show the scheme is partly practical for real scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

Enabling privacy preserving outsourced data aggregation is regarded as an important issue for multi-domain wireless networks. In this paper, we present a novel hybrid cloud based privacy preserving outsourced data aggregation framework. To achieve this, we introduce a hybrid storage cloud and aggregator cloud architecture, in which both of the storage clouds and aggregator cloud are assumed to be untrusted but they cannot collude with each other. Based on this security assumption, we propose two novel protocols, including the pro-active privacy preserving data aggregation and passive privacy preserving data aggregation schemes, which are based on the idea of secret sharing. The pro-active scheme allows the user to pro-actively split their data to multiple storage clouds to avoid data leaking while the passive scheme allows the users to store their encrypted data in storage cloud and aggregator to finish the data aggregation based on the encrypted data. The detailed performance simulations are given to demonstrate the security, effectiveness and efficiency of the proposed scheme.KeywordsPrivacy AggregationCloud ComputingParticipatory Sensing

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e., relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys "as-strong-as-possible" security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

Although searchable encryption schemes allow secure search over the encrypted data, they mostly support conventional Boolean keyword search, without capturing any relevance of the search results. This leads to a large amount of post-processing overhead to find the most matching documents and causes an unnecessary communication cost between the servers and end-users. Such problems can be addressed efficiently using a ranked search system that retrieves the most relevant documents. However, existing state-of-the-art solutions in the context of Searchable Symmetric Encryption (SSE) suffer from either (a) security and privacy breaches due to the use of Order Preserving Encryption (OPE) or (b) non-practical solutions like using the two non-colluding servers. In this paper, we present a generic solution for multi-keyword ranked search over the encrypted cloud data. The proposed solution can be applied over different symmetric searchable encryption schemes. To demonstrate the practicality of our technique, in this paper we leverage the Oblivious Cross Tags (OXT) protocol of Cash et al. (2013) due to its scalability and remarkable flexibility to support different settings. Our proposed scheme supports the multi-keyword search on Boolean, ranked and limited range queries while keeping all of the OXT’s properties intact. The key contribution of this paper is that our scheme is resilience against all common attacks that take advantage of OPE leakage while only a single cloud server is used. Moreover, the results indicate that using the proposed solution the communication overhead decreases drastically when the number of matching results is large.

Cross-lingual multi-keyword rank search with semantic extension over encrypted data

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud by users. To maintain the confidentiality of sensitive user data against untrusted servers, the data should be encrypted before they are uploaded. However, this raises a new challenge for performing search over the encrypted data efficiently. Although the existing searchable encryption schemes allow a user to search the encrypted data with confidentiality, these solutions cannot support the verifiability of searching result. We argue that a cloud server may be selfish in order to save its computation ability or bandwidth. For example, it may execute only a fraction of the search and returns part of the searching result. In this paper, we propose a new verifiable fuzzy keyword search scheme based on the symbol-tree which not only supports the fuzzy keyword search, but also enjoys the verifiability of the searching result. Through rigorous security and efficiency analysis, we show that our proposed scheme is secure under the proposed model, while correctly and efficiently realizing the verifiable fuzzy keyword search. The extensive experimental results demonstrate the efficiency of the proposed scheme.

An effective comparison protocol 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 exact keyword search. This significant drawback makes existing techniques unsuitable in cloud computing as it is greatly affect system usability, rendering user searching experiences very frustrating and system efficiency very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud while maintaining keyword privacy. In our solution, we exploit edit distance to quantify keyword similarity and develop new advanced technique on constructing fuzzy keyword sets which greatly reduces the storage and representation overheads. In this way, we show that our proposed solution is secure and privacy preserving, while realizing the goal of fuzzy keyword search.

Cloud Computing offers storage resources as well as network and computing resources to the organizations. This eliminates the high infrastructure cost for the organizations that are using these services as they can now dynamically pay for these services, i.e., pay per use model, which is followed by most of the cloud providers. As the organization does not locally host these resources, these are comparatively far easier to manage and use than the traditional infrastructural resources. As a result of these factors, the popularity of cloud computing is increasing continuously. But this transfer of data and applications to the cloud server also creates some challenges. It poses problems that must be dealt with properly to ensure a secure cloud computing environment. As more and more sensitive data is being uploaded on the cloud in the present scenario, the privacy and security concerns associated with the data is continuously increasing. To address this, issue the data is stored on the cloud in the encrypted form. Also, as the amount of data stored is usually tremendous, so an efficient search scheme is also necessary. So here, we deal with two significant aspects of cloud computing: Encryption and Searching. We are proposing a secure and efficient encryption scheme to encrypt the data stored in the cloud as well as the queries along with a multi-keyword search scheme to search over the encrypted cloud data.

Based on delegating quantum computing (DQC), a DQC model that adapts to multi-qubit and composite quantum circuits is given firstly. In this model, the single client with limited quantum ability can give her encrypted data to a powerful but untrusted quantum data server and let the data server computes over the encrypted data without decryption, where the computation is a quantum circuit composed of multiple quantum gates. Then, the client generates the decryption key to decrypt the computing result according to the circuit of computation. However, this model cannot meet the situation of multi-client accessing or computing encrypted cloud data in the cloud environment. To solve this problem, we let the client outsource key generation to a trusted key server, which composes the quantum cloud center with the data server. The clients only perform X and Z operation according to the encryption or decryption key. Then, combined with Grover algorithm, a quantum searchable encryption scheme for cloud data based on delegating quantum computing is proposed in this paper. The data server mainly uses Grover algorithm to perform search computation on the encrypted data. Moreover, a concrete example of our scheme is discussed next, where the data server searches for 2 target items from 8 items of the encrypted data. Finally, security of our proposed scheme is analysed, which can protect the security of the data.KeywordsQuantum searchable encryptionDelegating quantum computingUntrusted data serverTrusted key serverGrover algorithm

By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users' retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.