Comment on “Achieving Secure, Universal, and Fine-Grained Query Results Verification for Secure Search Scheme Over Encrypted Cloud Data”

SummaryIn a short signature scheme, the length of signature is much smaller than that of the general signature. Short signatures are widely used in low‐computation, low‐storage, and low‐bandwidth communication environments. The proof of security of all certificateless short signature schemes currently known is given in the random oracle model. In this paper, a new certificateless short signature scheme is constructed, and it is proved to be unforgeable in the standard model. Taken into account the computation costs, the scheme performs only one pairing operation, so it is more efficient than previous ones.

Secure conjunctive multi-keyword ranked search over encrypted cloud data for multiple data owners

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.

DNA similarity search has proven to be an essential demand in human genomic researches. Since DNA sequences contain many sensitive personal information, the acquisition and dissemination of DNA data have been tightly controlled and restricted by authorities. Although the problem of private DNA similarity query has been an active research issue, the latest research findings are still inadequate in terms of security, functionality and efficiency. In this paper, we propose an Efficient DNA Similarity Search scheme (EDSS) which can achieve fine-grained query and data access control over encrypted cloud data. Our original contributions are fourfold. First, we creatively put forward a private edit distance approximation algorithm to realize the efficient and high accurate DNA similarity query. Second, we classify the whole DNA sequences and design a multiple genes search strategy to achieve complicated logic query such as mixed “AND” and “NO” operations on genes. Third, the proposed scheme can also efficiently support data access control by employing a novel polynomial based design. Finally, security analysis and extensive experiments demonstrate the high security and efficiency of EDSS compared with existing schemes.

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.

With the advent of cloud computing, many organizations and individuals are interested in outsourcing their complex data management to the public cloud for economic savings and ease of access. As sensitive information may have to be encrypted before outsourcing, the data utilization service based on plaintext keyword search is not suitable for the encrypted cloud data. In this paper, we propose a solution for ranked semantic keyword search (RSS) over encrypted cloud data. With the design of semantic extension, the proposed scheme could return not only the exactly matched files, but also the files including the terms semantically related to the query keyword. In the proposed scheme, the data owner generates a piece of file metadata for each file, and uploads the encrypted metadata set and file collection to the cloud server. After receiving a query request, the cloud server first finds out the keywords that are semantically related to the query keyword according to SRL. Then both the query keyword and the extensional words are used to retrieve the files. Eventually, the result files are returned in order according to the total relevance score. Detailed security analysis shows that our solution is privacy-preserving and secure under the previous searchable symmetric encryption (SSE) security definition. Experimental evaluation demonstrates the efficiency and effectives of the scheme.

The inaugural Editor-in-Cheif (EiC) shares some of the open opportunities and challenges in cloud computing and then introduces the transactions and its progress. Since its launch early this year, IEEE Transactions on Cloud Computing (TCC) has been accepting submission of papers that introduce original and innovative ideas. That means submission of "extended versions" of already published works (e.g., conference/workshop papers) is not encouraged unless they contain significant number of "new and original" ideas/contributions along with more than 49% "brand new" material. TCC has been attracting submissions from academic researchers, industrial practitioners, policy and standards developers, and application communities from all over the world. During the last five months, we have received more than 150 submissions. In addition, four special issues have been scheduled to focus on specific challenges in cloud computing. They are also encouraging submission of revised versions of papers that are recognized as best papers from associated conferences. Along with EB members, I have been managing the content of the transactions and the rigorous peer review process to ensure publication of original and high-quality papers. Based on the quality, focus, and scope of each submitted paper, it either gets assigned to a suitable Associate Editor or desk rejected with suitable suggestions. Each paper that advances to the full review phase is assigned to an appropriate Associate Editor, who seeks three or more review reports from peers and experts in the field and makes an appropriate recommendation. The EiC has been able to make the final decision on each paper based on peer review reports and the recommendation of AE. The IEEE Computer Society is managing the manuscripts workflow from their initial submission to the final publication. The papers accepted for the first issue of TCC cover hot topics in Cloud computing ranging from Cloud security to energy-efficient resource provisioning.

Guest Editorial: Situational awareness of integrated energy systems

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.

Cloud computing is an interdisciplinary field which has witnessed exponential growth in recent years and there is a need to consolidate the research efforts. This study used citation analysis method to identify most cited papers which have achieved the status of citation classics. It is the first study using Dimensions.ai database to identify classics in cloud computing literature. Using a search strategy data was extracted from Dimensions database from 2010 and 2020. Finally 96 papers were identified as citation classics which were cited with at least 400 citations. Data was finally analyzed through Biblioshiny package of R Studio. It was found that Future Generation Computer Systems, IEEE Communication Survey and Tutorials, IEEE Transaction on Parallel and Distributed Systems, Computer and IEEE Access are the key journals contributing to classics. Most papers were contributed from China and USA. R. Buyya is the prolific author who has contributed nine citation classics.

Nowadays outsourcing data to cloud servers is a popular data management way for many users. But how to ensure the security and the usability of the outsourced data is a very challenge problem. Usually encryption of the outsourced data is a good way to protect the privacy. But after encryption, the usability always loses, such as how to search over the outsourced data. Aiming at solving this problem, recently Zhang et al. proposed a privacy preserving ranked multi-keyword search scheme for multiple data owners in cloud computing [IEEE Transactions on Computers, 65(5), 1566–1577, 2018]. However, in this short paper we find their scheme is not secure. Concrete, the adversary can forge the search trapdoor for any keyword, which invalidates the security goal of their original design.

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.

Mobile access and flexible search over encrypted cloud data in heterogeneous systems

Privacy-preserving verifiable (outsourced) computation (PPVC) is a useful technique for a resource-constrained client to outsource computationally heavy but sensitive tasks to a computationally powerful but untrusted worker and to obtain expected correct results from the worker. In this paper, we analyze the privacy property of three matrix masking-based PPVC protocols, which have recently been published in IEEE Transactions on Cloud Computing [2] , [3] . To do this, we present a formal definition of a privacy model for a PPVC protocol (see Definition 1 ), and then prove that neither of those three PPVC protocols holds privacy under this model. We also review the comments by Cao et al. [1] on two of the three protocols and show an issue in their comments.