Privacy-Preserving Outsourced Inner Product Computation on Encrypted Database

Yang, Haining; Su, Ye; Qin, Jing; Wang, Huaxiong

doi:10.1109/tdsc.2020.3001345

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…FE can provide privacy preservation on artificial intelligence (AI) applications. For example, Bahadori et al [BJMS21] On the other hand, IPFE is also used to protect outsourced computations on encrypted database against the untrusted cloud server [YSQW20]. Although IPFE is getting popular, the performance presented in previous work is still not very impressive, due to the its inherent heavy computations.…”

Section: Related Workmentioning

confidence: 99%

cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Han

Lee

Karmakar

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users' privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve huge amount of data. In this paper, we present a high performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24×, 40.02×, 156.30× and 18.76× speed-up for Setup, Encrypt, KeyGen and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12× faster than the AVX2 version that takes 23 seconds.

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Section: Related Workmentioning

confidence: 99%

cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Han

Lee

Karmakar

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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“…A homomorphic verifiable tag scheme is designed for blockless verification. Yang, et al, [27] developed a model for inner product computation using Inner Product Functional encryption (IPFE). There are two privacy weakness presented in the model such as master secret key and encrypted vector.…”

Section: Related Workmentioning

confidence: 99%

Securing the Information using Improved Modular Encryption Standard in Cloud Computing Environment

2023

KSII TIIS

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All aspects of human life have become increasingly dependent on data in the last few decades. The development of several applications causes an enormous issue on data volume in current years. This information must be safeguarded and kept in safe locations. Massive volumes of data have been safely stored with cloud computing. This technology is developing rapidly because of its immense potentials. As a result, protecting data and the procedures to be handled from attackers has become a top priority in order to maintain its integrity, confidentiality, protection, and privacy. Therefore, it is important to implement the appropriate security measures in order to prevent security breaches and vulnerabilities. An improved version of Modular Encryption Standard (IMES) based on layered modelling of safety mechanisms is the major focus of this paper's research work. Key generation in IMES is done using a logistic map, which estimates the values of the input data. The performance analysis demonstrates that proposed work performs better than commonly used algorithms against cloud security in terms of higher performance and additional qualitative security features. The results prove that the proposed IMES has 0.015s of processing time, where existing models have 0.017s to 0.022s of processing time for a file size of 256KB.

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“…Later, Cui et al [20] constructed an efficient scheme with partially hiding access policy based on linear secret sharing scheme (LSSS) in prime-order groups. Furthermore, attribute value is hidden by wildcards [21] and inner product encryption (IPE) [22]. To some extent, hiding attribute value can protect privacy, but attribute name can still reveal user information.…”

Section: Related Workmentioning

confidence: 99%

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

Liu¹,

Ji²,

Yin³

et al. 2021

KSII TIIS

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Personal health records (PHRs) is an electronic medical system that enables patients to acquire, manage and share their health data. Nevertheless, data confidentiality and user privacy in PHRs have not been handled completely. As a fine-grained access control over health data, ciphertext-policy attribute-based encryption (CP-ABE) has an ability to guarantee data confidentiality. However, existing CP-ABE solutions for PHRs are facing some new challenges in access control, such as policy privacy disclosure and dynamic policy update. In terms of addressing these problems, we propose a privacy protection and dynamic share system (PPADS) based on CP-ABE for PHRs, which supports full policy hiding and flexible access control. In the system, attribute information of access policy is fully hidden by attribute bloom filter. Moreover, data user produces a transforming key for the PHRs Cloud to change access policy dynamically. Furthermore, relied on security analysis, PPADS is selectively secure under standard model. Finally, the performance comparisons and simulation results demonstrate that PPADS is suitable for PHRs.

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Privacy-Preserving Outsourced Inner Product Computation on Encrypted Database

Cited by 10 publications

References 39 publications

cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

cuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Securing the Information using Improved Modular Encryption Standard in Cloud Computing Environment

Sharing and Privacy in PHRs: Efficient Policy Hiding and Update Attribute-based Encryption

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