Efficient and Privacy-Preserving Similarity Range Query Over Encrypted Time Series Data

Zheng, Yandong; Lu, Rongxing; Guan, Yunguo; Shao, Jun; Zhu, Hui

doi:10.1109/tdsc.2021.3061611

Cited by 49 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(L, 􏽢 p)-based decision problem [43]: given (k 0 , k 2 , N), the (L, 􏽢 p)-based decision problem is to determine whether an integer x ∈ Z N belongs to S or S without (􏽢 p, 􏽢 q, L), where…”

Section: Symmetric Homomorphic Encryption Mahdikhani Et Almentioning

confidence: 99%

Efficient Privacy-Preserving Data Aggregation Scheme with Fault Tolerance in Smart Grid

Yang

Zhao

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

As the traditional grid produces a large amount of greenhouse gas and cannot adapt to such new demands as dynamic electricity prices, data analysis, and early warning, smart grid with high efficiency and reliability is increasingly valued. It plays a key role in achieving carbon neutrality. Nonetheless, smart grid requires the collection of real-time power data, and personal privacy may be leaked through the frequent electricity measurement reports. With the requirements of data analysis and prediction while preserving users’ personal privacy, data aggregation schemes have emerged. However, existing schemes cannot resolve all the troubles well. Some schemes do not consider the failures for smart meters, and most of the schemes have expensive computation cost. In view of this, an efficient privacy-preserving data aggregation scheme with fault tolerance in smart grid is put forward in this paper. To be specific, the proposed scheme is lightweight due to the application of the symmetric homomorphic encryption technology and the elliptic curve cryptography. Even if some smart meters are destroyed, the proposed scheme can still successfully obtain aggregated data. Moreover, the proposed data aggregation scheme is proved to be secure, and all security requirements can be satisfied. Performance evaluation illustrates the relatively low computation cost and communication overhead of the proposed scheme compared to other related schemes.

show abstract

Section: Symmetric Homomorphic Encryption Mahdikhani Et Almentioning

confidence: 99%

Efficient Privacy-Preserving Data Aggregation Scheme with Fault Tolerance in Smart Grid

Yang

Zhao

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…e SHE [16,17] is a symmetric homomorphic encryption scheme, which mainly consists of three algorithms, namely, (i) key generation, (ii) encryption, and (iii) decryption:…”

Section: Description Of Shementioning

confidence: 99%

“…In this section, we will analyze the security of our proposed privacy-preserving biometric identification scheme. Since our proposed scheme is designed based on the SHE scheme, which has been proved to be CPA-secure in previous work [17], we mainly focus on the privacy-preserving goal described in section. Specifically, both CS1 and CS2 cannot obtain the plaintext of the biometric dataset and biometric identification request.…”

Section: Security Analysismentioning

confidence: 99%

“…In this paper, we propose an efficient and privacy-preserving biometric identification scheme based on two data structures, namely, the FITing-tree [14] and iDistance [15], and a symmetric homomorphic encryption (SHE) algorithm [16,17]. With our proposed scheme, the privacy of the biometric dataset and the identification request is preserved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

Yang

Zhu

Zhang

et al. 2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

show abstract

“…Due to security system vulnerabilities, up to 87 million Facebook users' private information was leaked, which contains name, contact information, search records, login location, etc. The disclosure of this sensitive information may lead to fraud, theft and other crimes [10]. Therefore, the problem of privacy disclosure needs to be solved urgently as it hinders the development of LBS [11].…”

Section: Introductionmentioning

confidence: 99%

A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Taking advantage of precise positioning technology, location-based service (LBS) has brought a lot of convenience to people’s daily life and made the city smarter. However, the LBS applications also bring some challenges to personal location privacy protection. In order to obtain services from LBS providers, users have to upload their queries including sensitive information, such as identities and locations. This information may be leaked out by the LBS providers or even eavesdropped on by malicious adversaries, which may cause privacy leakage. To tackle this problem, many solutions have been investigated under the assumption that users are uniformly distributed. However, the users are not always uniformly distributed in real-world situations. For a side-weight inference attack, the adversary would infer that the target user is more likely to belong to the road section with more users, resulting in performance deterioration. In this paper, we investigate the issue of location privacy preservation against side-weight inference attack for non-uniform distributed road network. Meanwhile, we consider the cost function of LBS and formulate the object as a mixed integer programming problem. Then, we propose a road truncation-based scheme to protect location privacy. The road section with high user density is designed to be truncated. Finally, simulation results show that our scheme meets the demand for privacy protection at a low cost. As a result, our scheme is proven to protect users’ location privacy effectively and efficiently.

show abstract

Efficient and Privacy-Preserving Similarity Range Query Over Encrypted Time Series Data

Cited by 49 publications

References 26 publications

Efficient Privacy-Preserving Data Aggregation Scheme with Fault Tolerance in Smart Grid

Efficient Privacy-Preserving Data Aggregation Scheme with Fault Tolerance in Smart Grid

An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack

Contact Info

Product

Resources

About