Encrypted Cloud Data Research Articles

SSF-CDW: achieving scalable, secure, and fast OLAP query for encrypted cloud data warehouse

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results.

Lightweight Searchable and Equality-Testable Certificateless Authenticated Encryption for Encrypted Cloud Data

Lightweight Searchable and Equality-Testable Certificateless Authenticated Encryption for Encrypted Cloud Data

VPRQ: Verifiable and privacy-preserving range query over encrypted cloud data

Cloud has scalable storage and massive computing power, attracting data owners to outsource their data. However, owing to privacy concerns, data is typically encrypted prior to outsourcing, which inevitably presents challenges for querying the data effectively. Although encrypted range query is one of the most prevalent types and has been widely studied, existing schemes still have some problems. They inadvertently disclose the order relationship between the upper/lower bound of a range query and the encrypted index, leading to vulnerability inference attack. Moreover, the cloud server cannot be fully trusted, which may return incorrect and incomplete query results. To deal with these issues, we present a novel verifiable and privacy-preserving range query scheme (VPRQ). The VPRQ scheme utilizes 0/1 technique to transform range comparisons into set intersections. By doing so, it effectively hides the relationship between the upper/lower bound and the encrypted index. On this basis, we design an encrypted garbled bloom filter to securely and effectively achieve range query. This ensures that VPRQ scheme effectively resists inference attack. Additionally, point-value polynomial function technology is integrated into the VPRQ protocol to provide lightweight verification for the query results. Comprehensive security analysis and proof demonstrate its effectiveness in achieving the intended design objectives. Performance evaluations illustrate the feasibility and scalability of the proposed scheme, highlighting its practical applicability.

Hierarchical Identity-Based Authenticated Encryption with Keyword Search over encrypted cloud data

With the rapid development of cloud computing technology, cloud storage services are becoming more and more mature. However, the storage of sensitive data on remote servers poses privacy risks and is presently a source of concern. Searchable Encryption (SE) is an effective method for protecting sensitive data while preserving server-side searchability. Hierarchical Public key Encryption with Keyword Search (HPEKS), a new variant of SE, allows users with higher access permission to search over encrypted data sent to lower-level users. To the best of our knowledge, there exist only four HPEKS schemes in the literature. Two of them are in traditional public-key setting, and the remaining ones are identity-based public key cryptosystems. Unfortunately, all of the four existing HPEKS schemes are vulnerable against inside Keyword Guessing Attacks (KGAs). Moreover, all of the existing HPEKS schemes are based on the computationally expensive bilinear pairing operation which dramatically increases the computational costs. To overcome these issues, in this paper, we introduce the notion of Hierarchical Identity-Based Authenticated Encryption with Keyword Search (HIBAEKS). We formulate a security model for HIBAEKS and propose an efficient pairing-free HIBAEKS scheme. We then prove that the proposed HIBAEKS scheme is secure under the defined security model and is resistant against KGAs. Finally, we compare our proposed scheme with related constructions regarding security requirements, computational and communication costs to indicate the overall superiority of our proposed scheme.

EPSMR: An efficient privacy-preserving semantic-aware multi-keyword ranked search scheme in cloud

Traditional privacy-preserving search schemes have commonly used the TF-IDF model, which relies on keyword frequency statistics but neglects the semantic relationships between and documents. In recent years, a few secure multi-keyword ranked search schemes over encrypted cloud data have considered the data semantics. However, the search efficiency of these schemes can still be improved. This paper presents an efficient privacy-preserving semantic-aware multi-keyword ranked search scheme (EPSMR) over encrypted cloud data. EPSMR utilizes the LDA topic model to discover hidden semantic information in documents and convert documents into vector representations. vector encryption by using the secure inner product is employed to implement the privacy-preserving semantic relevance score computation between search keywords and documents. To achieve efficient multi-keyword searches, the bisecting k-means algorithm and search tree structure are introduced to design a novel ω-way balanced search tree index which benefits finding search results quickly. to further improve the search efficiency, a filtering-threshold lookup table is presented, which can be used to prune unqualified sub-trees in advance. The experimental results show the superior performance of the proposed scheme compared to existing schemes in terms of both semantic search precision and search time cost.

Blockchain for Achieving Accountable Outsourcing Computations in Edge Computing

Since the emergence of cloud computing, data owners are being driven to move their intricate data management systems from on-site locations to private cloud providers in order to take advantage of increased flexibility and cost savings. However, sensitive data must be encrypted before being outsourced in order to safeguard data privacy. This renders outdated the conventional method of using data, which relies on plaintext keyword searches. It is therefore crucial to activate an encrypted cloud data search service. In order to meet the effective data retrieval, need, search services must support multi-keyword queries and offer result similarity rating, given the volume of data users and documents stored in cloud storage. Similar works on searchable encryption seldom distinguish between search results and concentrate on single keyword or Boolean keyword search. I define and solve the difficult problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE) for the first time in this work. I also outline a tight set of privacy requirements that must be met in order for such a safe cloud data utilization system to be implemented.

Efficient keyword search on encrypted dynamic cloud data

Sensitive information is increasingly being outsourced to the cloud. In order to protect the privacy of such sensitive data, cloud users (clients) encrypt their data before outsourcing. However, this poses a difficulty to later perform search operations on the encrypted data. Searchable encryption schemes enable a client to search and retrieve the cloud data (based on the keywords present in the data) when the data is encrypted. Dynamic searchable encryption schemes allow the client to search over the encrypted cloud data even when new documents are added to or deleted from the encrypted data. There is a trade-off between security (that is measured in terms of information leaked to the cloud) and the efficiency of dynamic searchable encryption schemes. Stronger security guarantees often come at a cost of less efficiency.In this work, we propose a new dynamic searchable encryption scheme for cloud data that achieves better security guarantees and improved efficiency compared to popular dynamic searchable encryption schemes. Our scheme uses an efficient data structure that reduces storage, lookup (search) time, and database modification time. We build a prototype of our scheme and experiment on large real-life datasets. We show our scheme performs better than the existing schemes, which provide similar (or weaker) security.

Efficient and Privacy-Preserving Encode-Based Range Query Over Encrypted Cloud Data

Efficient and Privacy-Preserving Encode-Based Range Query Over Encrypted Cloud Data

A lattice-based designated-server public-key authenticated encryption with keyword search

Public-key Encryption with Keyword Search (PEKS) provides an efficient way to search on encrypted cloud data. However, it cannot resist against off-line Inside Keyword Guessing Attacks (IKGA). To alleviate this issue, Huang and Li proposed the Public-key Authenticated Encryption with Keyword Search (PAEKS), which the keyword is also authenticated. However, they do not provide post-quantum security promises. In this paper, we propose the first lattice-based designated-server PAEKS (dPAEKS). We formally define dPAEKS and its security models, and propose a lattice-based construction. It is quantum-resistant under the assumption of learning with errors and the sender authenticated the keyword so it can resist IKGA. Furthermore, we strengthen the security requirement, so that only the designated tester can search the ciphertext. Finally, we compare our scheme with some related schemes to demonstrate its efficiency.

Privacy-Preserving Dynamic Multi-Keyword Ranked Search Scheme in Multi-User Settings

With the rapid development of consumer electronics in Industry 5.0, personalized service supplement based on the cloud infrastructure for the Internet of Things (IoT) has been a promising requirement pushed to consumers. During the massive and frequent IoT data interaction from the consumer-centric in Industry 5.0, efficiently searchable encryption for multiple consumers are indispensable. Existing multi-user searchable encryption is based on attribute and predicate encryption technology to realize one-to-many rather than realistic many-to-many scenarios. Moreover, multi-keyword ranked search, dynamic update, and search pattern hiding have not been implemented in the multi-user scenario. In this paper, to address the above problems for consumer electronics in Industry 5.0, we present a privacy-preserving dynamic multi-keyword ranked search scheme over encrypted cloud data to accomplish pay-as-you-consume cloud data security sharing. Our scheme designs a specific tree-based index structure and a “Greedy Breadth-First Search” algorithm to achieve the sub-linear search. To support dynamic updates in the cloud, a novel secure maximum generation protocol is proposed. Finally, the security analysis and experiment evaluation prove our scheme cannot only preserve the privacy of index and search patterns but also support dynamic update operations under the multi-writer/multi-reader setting at an acceptable cost.

AI-Powered Anomaly Detection in Encrypted Cloud Data

This research paper analyzes cryptographic techniques, performance considerations, security challenges, and compliance requirements associated with CMEK. The research also delves into advanced concepts and future directions, including homomorphic encryption and blockchain-based key management. CMEK offers a solution by allowing customers to retain control over their encryption keys while leveraging cloud infrastructure. This study explores the architecture, implementation, and implications of CMEK across various cloud service models.

Integrated Approach for Providing Data Security Verification Over Encrypted Cloud Data

The cloud has recently attracted a lot of user attention from both small and large businesses, including those in software, BPO, healthcare, schools, colleges, and many other industries. For data storage and access from remote places linked to one another from a central server with the aid of the internet, all of these organisations attempt to adopt this centralised cloud server. Because all data is stored remotely and only occasionally accessed locally, the confidentiality of data is crucial to cloud service providers. As is common knowledge, no cloud service provider currently offers data privacy through encryption and message digests to enable data authorisation. almost all businesses An untrusted user can query data files of interest by sending encrypted keywords as a search query to the cloud server. Try to search the data in a secure manner over encrypted cloud data. In this dishonest cloud environment, the returned query results may occasionally be correct, incorrect, or partial. As everyone is aware, cloud servers today nearly actively withhold some qualified results in order to conserve computational resources and communication overhead. In this research, we presented and analysed a safe, practical, and fine-grained query results verification mechanism, by which the query user, given an encrypted query results set, not only can confirm the integrity of each data file, but also determines the overall number of qualifying data files, which are not returned if the set is not finished before the decryption procedure. This served as our primary inspiration for creating a brand-new secure verification object for encrypted cloud storage. Here, the short signature key is created using the message digest method MD5, which is also used to confirm the data's authenticity. We tested our proposed model in a variety of ways, and the results demonstrate that it is a useful and effective system

Verifiable and Searchable Symmetric Encryption Scheme Based on the Public Key Cryptosystem

With the rapid development of Internet of Things technology and cloud computing technology, all industries need to outsource massive data to third-party clouds for storage in order to reduce storage and computing costs. Verifiable and dynamic searchable symmetric encryption is a very important cloud security technology, which supports the dynamic update of private data and allows users to perform search operations on the cloud server and verify the legitimacy of the returned results. Therefore, how to realize the dynamic search of encrypted cloud data and the effective verification of the results returned by the cloud server is a key problem to be solved. To solve this problem, we propose a verifiable dynamic encryption scheme (v-PADSSE) based on the public key cryptosystem. In order to achieve efficient and correct data updating, the scheme designs verification information (VI) for each keyword and constructs a verification list (VL) to store it. When dynamic update operations are performed on the cloud data, it is easy to quickly update the security index through obtaining the latest verification information in the VL. The safety and performance evaluation of the v-PADSSE scheme proved that the scheme is safe and effective.

Feature Matching Search Mechanism Over Encrypted Cloud Data

Abstract: In recent years, there has been a lot of research done on encrypted search technologies. Creating indexes with separate keywords has led to high storage costs and poor search accuracy, which has made the issue urgently need to be solved as more and more data is being stored in the cloud. So, for encrypted cloud data, we provide a novel feature matching ranked search method (FMRSM) in this study. This approach builds indexes using the feature score algorithm (FSA), which enables multiple keywords to be mapped to an index dimension after being taken from a page as a feature. As a result, index storage costs may be decreased and encryption effectiveness can be increased. Furthermore, the FMRSM generates trapdoor processes using a matching score algorithm (MSA). The matching score algorithm is able to produce results with improved ranking accuracy because to FSA, which allows it to rank the search results according to the type of match and the amount of matching terms. A thorough investigation shows that our mechanism is more practical and efficient

Practical and Dynamic Attribute-Based Keyword Search Supporting Numeric Comparisons Over Encrypted Cloud Data

The attribute-based keyword search (ABKS), which simultaneously achieves searching and fine-grained access control over encrypted data, is frequently applied in cloud computing environments characterized by data storage and sharing. Recently, inspired by attribute-based encryption (ABE) and searchable encryption (SE) primitives, several ABKS schemes have been presented. However, almost all existing ABKS schemes actually only provide an attribute-based keyword equality match function and do not have a structural index to support practical search efficiency and dynamic data updates in real-world applications. To the best of our knowledge, this study is the first to realize an attribute-based keyword search construction supporting numerical comparison expressions with the practical search efficient and dynamic data update capacity (ABKS-NICEST), based on our proposed attribute-based keyword secure search scheme supporting numerical comparison expressions (ABKS-NICE) and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exclusive OR-chain</i> -based inverted index structure. To the best of our knowledge, ABKS-NICEST is the first attributed-based keyword search scheme with practical search efficiency and dynamic data update capacity. In addition, numerical values are an important and common attribute, so providing comparison expressions among numerical values can greatly enhance the expressivity of access policy. Therefore, we use the prefix membership verification technique to design a method to support any numeric comparison expression in a flexible and uniform manner. Through theoretical and experimental evaluations, we determine that ABKS-NICEST is the most efficient ABKS scheme.

SecDedup: Secure data deduplication with dynamic auditing in the cloud

In the era of big data, data explosion has brought challenges to cloud storage management. To improve cloud storage efficiency and save network communication bandwidth, cloud data deduplication has emerged as a research hotspot, especially in the field of encrypted cloud data storage. How to enhance the security of encrypted data deduplication by resisting various attacks on deduplication has become an important research issue. However, existing solutions suffer from security flaws and are vulnerable to a series of attacks, e.g., duplicate faking attacks, file ownership spoofing attacks, and file tampering attacks. Besides, dynamic data operation is rarely considered or audited. To solve the above problems, we propose a novel scheme, named SecDedup, to enhance the security of encrypted cloud data deduplication with dynamic auditing. SecDedup applies a homomorphic authenticator and designs a multi-functional data tag with optimized storage to support deduplication and auditing at the same time with security guarantee against various attacks as mentioned above. In particular, We embed multi-set hash functions into data tags to achieve dynamic data auditing. In addition, SecDedup supports batch auditing with optimized computational cost for multiple deduplication auditing tasks. We formally prove the correctness and security of SecDedup, showing that it can successfully achieve our design goals for resisting the above listed attacks. We also analyze and evaluate the performance of SecDedup in terms of computation, communication, and tag storage overheads by comparing them with existing works. The results show its effectiveness and scalability.

A novel semantic-aware search scheme based on BCI-tree index over encrypted cloud data

A novel semantic-aware search scheme based on BCI-tree index over encrypted cloud data

Keyword search over dynamic encrypted cloud data using symmetric key based verification

Keyword search over dynamic encrypted cloud data using symmetric key based verification

Efficient and Privacy-Preserving Similar Patients Query Scheme Over Outsourced Genomic Data

Over the past decade, genomic data has grown exponentially and is widely used in promising medical and health-related applications, which opens up new opportunities for the field of medicine. Similar patients query (SPQ), which can help physicians formulate an optimal therapy, is one of such popular applications. Despite its popularity, since human genomes are usually highly sensitive, a series of policies have been launched by the government to strictly control its acquisitions and utilization. Thus, how to prevent privacy disclosure becomes of great importance to the flourish of SPQ services. In this paper, aiming at the above challenge, we first design a novel genetic BK-tree (GBK-tree) for a genomic database. Then, combined with a random sorting mechanism and some existing encryption techniques, we propose an efficient and privacy-preserving similar patients query scheme over encrypted cloud data, named CASPER. With CASPER, a medical institution can securely outsource its private genomic database to a cloud server, and physicians can request SPQ services from the cloud server while keeping her/his query secret. Detailed security analysis shows that CASPER can preserve privacy in the presence of different threats. Furthermore, extensive performance evaluations demonstrate the high accuracy and efficiency of our proposed scheme.

Secure Multi-Level Privacy-Protection Scheme for Securing Private Data over 5G-Enabled Hybrid Cloud IoT Networks

The hybrid cloud is a secure alternative for enterprises to exploit the benefits of cloud computing to overcome the privacy and security concerns of data in IoT networks. However, in hybrid cloud IoT, sensitive items such as keys in the private cloud can become compromised due to internal attacks. Once these keys are compromised, the encrypted data in the public cloud are no longer secure. This work proposes a secure multilevel privacy-protection scheme based on Generative Adversarial Networks (GAN) for hybrid cloud IoT. The scheme secures sensitive information in the private cloud against internal compromises. GAN is used to generate a mask with the input of sensory data-transformation values and a trapdoor key. GAN’s effectiveness is thoroughly assessed using Peak Signal-to-Noise Ratio (PSNR), computation time, retrieval time, and storage overhead frameworks. The obtained results reveal that the security scheme being proposed is found to require a negligible storage overhead and a 4% overhead for upload/retrieval compared to the existing works.