Privacy Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism

Wang, Xin; He, Jianping; Cheng, Peng; Chen, Jiming

doi:10.1109/tsp.2018.2880722

Cited by 40 publications

(20 citation statements)

References 40 publications

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“…Docker, Kubernetes. A two phased framework supporting heterogeneous privacy for personal data was proposed in [9]. The nodes hosting user data utilised one-shot noise perturbation to reach heterogeneous privacy protection over various data servers.…”

Section: Fig 1: Overview Of the Research Approachmentioning

confidence: 99%

Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Barati

Aujla

Llanos

et al. 2022

IEEE Trans. Ind. Inf.

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Emerging multi-tenant cloud computing ecosystems allow multiple applications to share virtualised pool of computing and networking resources. As a result such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorised access). While cloud computing providers support robust security and privacy mechanisms (e.g, public key cryptography, firewalls, virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation (GDPR) in Europe and the Data Protection Act (DPA) in the UK, further emphasise the need to overcome these privacy limitations. A novel technique for monitoring, auditing and verifying the operations carried out on a user's personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., Blockchain, Smart Contracts) for developing an immutable recording technique, which transparently logs, monitors and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests (> 13K) ensuring minimal latency (≈50-60 ms) and overheads for three different service packages varied with respect to the number of actors and operations).

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Section: Fig 1: Overview Of the Research Approachmentioning

confidence: 99%

Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Barati

Aujla

Llanos

et al. 2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

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“…Afifi and Zhou et al in [9] constructed a new multivariate privacy feature quantification model, analyzed the sensitivity of identifiers, and proposed two different measurement methods to quantify privacy disclosure; however, there is a lack of optimization research on the information publication. Wang and He et al in [10] proposed a two-stage framework to calculate the average value, which can achieve the optimal calculation accuracy on the premise of meeting the privacy requirements; however, the influence of the node on the calculation accuracy still needed further study.…”

Section: A Privacy Metrics and Managementmentioning

confidence: 99%

“…ij q represents the ith service and jth attribute, we can get the following expression: (9) In the attributes matrix QoS , the larger the value of some attributes, the lower the quality of service; the larger the value of some attributes, the higher the quality of service, such as confidentiality, reliability, etc. These values need to be normalized, each element in the QoS can be normalized in formula (10): The normalized attribute value is used to calculate the QoS , as shown in formula (11):…”

Section: A Service Trustmentioning

confidence: 99%

Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

Han

2021

IEEE Access

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To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

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“…Another recent contribution [34] proposes a hybrid architecture where a set of servers collect data from a set of nodes and compute the average. The paper focuses on providing nodes with heterogeneous privacy guarantees with respect to different privacy violators.…”

Section: Averaging Performancementioning

confidence: 99%

Robust Privacy-Preserving Gossip Averaging

Pilet

Frey

Taı̈ani

2019

Lecture Notes in Computer Science

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Decentralized solutions are emerging as promising candidates to overcome the privacy risks associated with centralized data services. Such solutions suffer however from their own range of privacy vulnerabilities, arising from untrusted and malicious peers. In this paper, we consider the emblematic problem of privacy-preserving decentralized averaging, and propose a novel gossip protocol that exchanges noise for several rounds before starting to exchange actual data. This makes it hard for an honest but curious attacker to know whether a user is transmitting noise or actual data. Our protocol and analysis do not assume a lock-step execution, and demonstrate improved resilience to colluding attackers. We prove the correctness of this protocol as well as several privacy results. Finally, we provide simulation results about the efficiency of our averaging protocol.

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Privacy Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism

Cited by 40 publications

References 40 publications

Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Research on Adaptive Relationship Between Trust and Privacy in Cloud Service

Robust Privacy-Preserving Gossip Averaging

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