A multistage protocol for aggregated queries in distributed cloud databases with privacy protection

Kelarev, AV; Yi, Xun; Badsha, Shahriar; Yang, Xu; Rylands, Leanne; Seberry, Jennifer

doi:10.1016/j.future.2018.08.017

Cited by 19 publications

(11 citation statements)

References 70 publications

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“…), while others incorporate homomorphic encryption schemes, pseudonymization, l-diversity, t-closeness, differential privacy, etc. [7]. Each of them has its advantages and shortcomings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Over the years, several research works have been conducted on the data privacy-preservation mechanisms (e.g., secure multi-party communication, k-anonymization, l-diversity, etc.) [7] and the privacy-violating attacks on those mechanisms [8]- [10]. To overcome such privacy-violating attacks while preserving data confidentiality and maintaining a level of data utility, the concept of DP (Differential Privacy) has been introduced by Dwork et al [11].…”

Section: Introductionmentioning

confidence: 99%

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Privacy, Security, and Utility Analysis of Differentially Private CPES Data

Tamjid¹,

Badsha²,

Shen³

2021

Preprint

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Differential privacy (DP) has been widely used to protect the privacy of confidential cyber physical energy systems (CPES) data. However, applying DP without analyzing the utility, privacy, and security requirements can affect the data utility as well as help the attacker to conduct integrity attacks (e.g., False Data Injection(FDI)) leveraging the differentially private data. Existing anomaly-detection-based defense strategies against data integrity attacks in DP-based smart grids fail to minimize the attack impact while maximizing data privacy and utility. To address this challenge, it is nontrivial to apply a defensive approach during the design process. In this paper, we formulate and develop the defense strategy as a part of the design process to investigate data privacy, security, and utility in a DP-based smart grid network. We have proposed a provable relationship among the DP-parameters that enables the defender to design a faulttolerant system against FDI attacks. To experimentally evaluate and prove the effectiveness of our proposed design approach, we have simulated the FDI attack in a DP-based grid. The evaluation indicates that the attack impact can be minimized if the designer calibrates the privacy level according to the proposed correlation of the DP-parameters to design the grid network. Moreover, we analyze the feasibility of the DP mechanism and QoS of the smart grid network in an adversarial setting. Our analysis suggests that the DP mechanism is feasible over existing privacy-preserving mechanisms in the smart grid domain. Also, the QoS of the differentially private grid applications is found satisfactory in adversarial presence.

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“…), while others incorporate homomorphic encryption schemes, pseudonymization, l-diversity, t-closeness, differential privacy, etc. [7]. Each of them has its advantages and shortcomings.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy, Security, and Utility Analysis of Differentially Private CPES Data

Tamjid¹,

Badsha²,

Shen³

2021

Preprint

Self Cite

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“…Early SE schemes focus on private single keyword retrieval over encrypted documents [19]. Afterwards, many SE schemes were proposed to support various search scenarios including multiple keywords search [20], conjunctive keywords search [21], distributed search [22], [23] and similarity search [24]- [26]. However, most of them are not suitable for encrypted image retrieval tasks.…”

Section: Related Workmentioning

confidence: 99%

Toward Efficient Encrypted Image Retrieval in Cloud Environment

Huang

Zhang

2019

IEEE Access

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Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes. INDEX TERMS Content-based image retrieval, encrypted image retrieval, navigable small world graph, secure index.

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“…An efficient noise addition framework for privacy preserving data mining was proposed in [11][12][13][14]. An algorithm for the private processing of distributed queries was proposed in [15]. The present paper considers a larger class of queries.…”

Section: Introductionmentioning

confidence: 99%

“…The present paper considers a larger class of queries. The algorithm proposed in [15] was the first and only algorithm applicable in the situation considered in the present paper. However, the procedure proposed in [15] does not provide protection against active attackers and the previous algorithm cannot solve the problem considered in the present paper.…”

Section: Introductionmentioning

confidence: 99%

Privacy-enhancing data aggregation and data analytics in wireless networks for a large class of distributed queries

Yang

Kelarev

2022

Wireless Netw

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Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS.

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A multistage protocol for aggregated queries in distributed cloud databases with privacy protection

Cited by 19 publications

References 70 publications

Privacy, Security, and Utility Analysis of Differentially Private CPES Data

Privacy, Security, and Utility Analysis of Differentially Private CPES Data

Toward Efficient Encrypted Image Retrieval in Cloud Environment

Privacy-enhancing data aggregation and data analytics in wireless networks for a large class of distributed queries

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