Privacy-Preserving Indexing and Query Processing for Secure Dynamic Cloud Storage

Du, Minxin; Wang, Qian; He, Meiqi; Weng, Jian

doi:10.1109/tifs.2018.2818651

Cited by 73 publications

(31 citation statements)

References 29 publications

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“…Systems relying on homomorphic encryption [11], [13], [16], [27], [28], [29] are often limited in the functionalities they offer (e.g., sum only). They present high-performance overhead in comparison with their less secure counterparts or still rely on honest-but-curious parties.…”

Section: Related Workmentioning

confidence: 99%

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Froelicher

Troncoso-Pastoriza

Sousa

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.

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Section: Related Workmentioning

confidence: 99%

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Froelicher

Troncoso-Pastoriza

Sousa

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…Thus, a practical SSE has to allow some information leakage in exchange for acceptable efficiency. Unfortunately, these leakages have been abused to attack SSE schemes in different ways [14,15,29,38,39]. In 2016, Zhang et al [16] proposed the file-injection attack.…”

Section: The Need For Forward Privacymentioning

confidence: 99%

Searchable Symmetric Encryption with Forward Search Privacy

Huang

et al. 2021

IEEE Trans. Dependable and Secure Comput.

149

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Searchable symmetric encryption (SSE) has been widely applied in the encrypted database for queries in practice. Although SSE is powerful and feature-rich, it is always plagued by information leaks. Some recent attacks point out that forward privacy which disallows leakage from update operations, now becomes a basic requirement for any newly designed SSE schemes. However, the subsequent search operations can still leak a significant amount of information. To further strengthen security, we extend the definition of forward privacy and propose the notion of "forward search privacy". Intuitively, it requires search operations over newly added documents do not leak any information about past queries. The enhanced security notion poses new challenges to the design of SSE. We address the challenges by developing the hidden pointer technique (HPT) and propose a new SSE scheme called Khons, which satisfies our security notion (with the original forward privacy notion) and is also efficient. We implemented Khons and our experiment results on large dataset (wikipedia) show that it is more efficient than existing SSE schemes with forward privacy.

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“…It means that users can update the encrypted indices and files securely. Many previous works can achieve this goal [24,25]. In our scheme, we adopt the forward index structure in which each file has its index.…”

Section: More Commentsmentioning

confidence: 99%

“…The SSE scheme supporting Boolean keywords search with sublinear search time was given in [24]. In order to support queries with rich expressiveness, SSE schemes supporting media data and similarity keywords search are introduced in [25,26]. More important, these works also support dynamic update.…”

Section: Introductionmentioning

confidence: 99%

Secure and Efficient Searchable Public Key Encryption for Resource Constrained Environment Based on Pairings under Prime Order Group

Zhang

Wang

2019

Security and Communication Networks

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Searchable public key encryption scheme is a key technique for protecting data confidentiality in today’s cloud environment. Specifically, public key encryption with conjunctive and disjunctive keyword search (PECDK) can provide flexible search options without sacrificing keywords security and thus attracts a lot of attention nowadays. However, the most effective PECDK scheme is based on the inner product encryption (IPE), which needs more time and space cost. In this paper, by utilizing the bilinear pairing with a prime order group, we propose an efficient PECDK scheme needing less time and storage consumption. The proposed scheme is proven to be secure under a rigorous security definition. The theoretical analysis and experimental results demonstrate that our proposed scheme can significantly improve the time and space efficiency over the state-of-the-art scheme.

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Privacy-Preserving Indexing and Query Processing for Secure Dynamic Cloud Storage

Cited by 73 publications

References 29 publications

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Searchable Symmetric Encryption with Forward Search Privacy

Secure and Efficient Searchable Public Key Encryption for Resource Constrained Environment Based on Pairings under Prime Order Group

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