A Differential Privacy-Based Query Model for Sustainable Fog Data Centers

Du, Miao; Wang, Kun; Liu, Xiulong; Guo, Song; Zhang, Yan

doi:10.1109/tsusc.2017.2715038

Cited by 39 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [12] proposed a three-layer privacy protection framework; however, they did not consider the problem of local equipment failure, in which case users would not be able to query complete data. Du et al [13] proposed a fog computing support data center query model based on differential privacy and proved, through rigorous mathematical deduction, that it ensured the reliability and effectiveness of privacy protection. Lyu et al [14] proposed the PPFA privacy protection aggregation system that uses the stability of a Gauss mechanism to ensure the differential privacy of the statistical results and reduces the loss of privacy by combining a stream cipher with a public key cipher to maintain practicability.…”

Section: Sensitive Data Protection Based On Fog Computingmentioning

confidence: 99%

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Liu

Yuan

Lai

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

Industrial Internet technology has developed rapidly, and the security of industrial data has received much attention. At present, industrial enterprises lack a safe and professional data security system. Thus, industries urgently need a complete and effective data protection scheme. This study develops a three-layer framework with local/fog/cloud storage for protecting sensitive industrial data and defines a threat model. For real-time sensitive industrial data, we use the improved local differential privacy algorithm M-RAPPOR to perturb sensitive information. We encode the desensitized data using Reed–Solomon (RS) encoding and then store them in local equipment to realize low cost, high efficiency, and intelligent data protection. For non-real-time sensitive industrial data, we adopt a cloud-fog collaborative storage scheme based on AES-RS encoding to invisibly provide multilayer protection. We adopt the optimal solution of distributed storage in local equipment and the cloud-fog collaborative storage scheme in fog nodes and cloud nodes to alleviate the storage pressure on local equipment and to improve security and recoverability. According to the defined threat model, we conduct a security analysis and prove that the proposed scheme can provide stronger data protection for sensitive data. Compared with traditional methods, this approach strengthens the protection of sensitive information and ensures real-time continuity of open data sharing. Finally, the feasibility of our scheme is validated through experimental evaluation.

show abstract

Section: Sensitive Data Protection Based On Fog Computingmentioning

confidence: 99%

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Liu

Yuan

Lai

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Yao [12] introduced the concept of α-mutual information security and showed that statistical security meant mutual information security. Du and Wang [13] proposed a query model and implemented differential privacy by Laplace noise. Tsou and Chen [17] quantified the disclosure risk and linked the differential privacy with k-anonymity.…”

Section: Research On the Basicmentioning

confidence: 99%

“…At present, most of the proposed privacy protection schemes use anonymous fuzzy or data distortion processing (such as adding random noise) and other technologies and use mathematical regression analysis, data distortion adjustment, and noise scale parameter adjustment to reduce the error caused by noise, so as to improve the availability of data [12][13][14]. However, these schemes also have some shortcomings; that is, the same query results will cause the disclosure of privacy information when the query users with different permissions and reputation levels query the sensitive data.…”

Section: Introductionmentioning

confidence: 99%

Research on Selection Method of Privacy Parameter $ε$

Sun

2020

Security and Communication Networks

View full text Add to dashboard Cite

Budget factor is an important factor to measure the intensity of differential privacy, and its allocation scheme has a great impact on privacy protection. This paper studies the selection of the parameter ε in several cases of differential privacy. Firstly, this paper proposes a differential privacy protection parameter configuration method based on fault tolerance interval and analyzes the adversaryʼs fault tolerance under different noise distribution location parameters and scale parameters. Secondly, this paper proposes an algorithm to optimize the application scenarios of multiquery, studies the location parameters and scale parameters in detail, and proposes a differential privacy mechanism to solve the multiuser query scenarios. Thirdly, this paper proposes the differential privacy parameter selection methods based on the single attack and repeated attacks and calculates the upper bound of the parameter ε based on the sensitivity Δ q , the length of the fault tolerance interval L , and the success probability p as long as the fault tolerance interval. Finally, we have carried out a variety of simulation experiments to verify our research scheme and give the corresponding analysis results.

show abstract

“…The solution preserves data confidentiality, integrity, mutual authentication, privacy, and anonymity. Differential privacy preserving query model is proposed in [13], and Laplacian noise is added to ensure the robustness of the proposed solution. In [14], a secure fog orchestrator is presented based on secure-bydesign protocols.…”

Section: Related Workmentioning

confidence: 99%

Preserving data security in distributed fog computing

et al. 2019

View full text Add to dashboard Cite

In this paper, a novel cryptographic solution is proposed to secure data in fog computing. The solution combines the AES-GMAC operation mode with information dispersal over GF(2 w) to provide data confidentiality, integrity, and availability along with source authentication. The value of w is flexible (8, 16, 32 or 64) and it could be configured according to the fog device features. Moreover, the proposed cryptographic solution is based on the dynamic key-dependent approach, which allows for a good compromise between the security level and computational complexity. In the proposed solution, the collected data at one fog node is encrypted, authenticated and dispersed in a pseudorandom manner to its n neighbor fog nodes. For data recovery, any k of the n fragments along with the corresponding dynamic key are required to retrieve the original data. This complicates the attacker's task who needs to compromise at least k fog nodes to disclose the encrypted data. Additionally, attackers should seek the dynamic key, which is different for each input data. On the other hand, redundant fragments protect the stored data against up to (n − k) fog nodes' failure or unavailability. The security and performance analysis tests show that the proposed security scheme exhibits a high level of efficiency and robustness.

show abstract

A Differential Privacy-Based Query Model for Sustainable Fog Data Centers

Cited by 39 publications

References 42 publications

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Research on Selection Method of Privacy Parameter $ε$

Preserving data security in distributed fog computing

Contact Info

Product

Resources

About

A Differential Privacy-Based Query Model for Sustainable Fog Data Centers

Cited by 39 publications

References 42 publications

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Protection of Sensitive Data in Industrial Internet Based on Three-Layer Local/Fog/Cloud Storage

Research on Selection Method of Privacy Parameter ε

Preserving data security in distributed fog computing

Contact Info

Product

Resources

About

Research on Selection Method of Privacy Parameter $ε$