Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

Gu, Bruce; Gao, Longxiang; Wang, Xiaodong; Qu, Youyang; Jin, Jiong; Yu, Shui

doi:10.1109/tnse.2019.2933639

Cited by 39 publications

(9 citation statements)

References 25 publications

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“…It is due to the constant quality of service, and it controls the payoff discount in case of a fixed strategy. In the case of the MDP approach (Gu B (2019)), it is the same as the myopic strategy.…”

Section: Analysis Of Performance Metrics For the Proposed Modelmentioning

confidence: 99%

An adaptive approach for preserving privacy in context aware applications for smartphones in cloud computing platform

Gadiyar

G.S

Goudar

2023

Preprint

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With the widespread use of mobile phones and smartphone applications, protecting one’s privacy has become a major concern. Because active defensive strategies and temporal connections between situations relevant to users are not taken into account, present privacy preservation systems for cell phones are often ineffective. This work defines secrecy maintenance issues similar to optimizing tasks, thereby verifying their accuracy and optimization capabilities through a hypothetical study. Many optimal issues arise while preserving one’s privacy and these optimal issues are to be addressed as linear programming issues. By addressing linear programming issues, an effective context-aware privacy-preserving algorithm (CAPP) was created that uses an active defence strategy to determine how to release a user’s current context to enhance the quality of service (QoS) regarding context-aware applications while maintaining secrecy. CAPP outperforms other standard methodologies in lengthy simulations of actual data. Additionally, the minimax learning algorithm (MLA) optimizes the policy users and improves the satisfaction threshold of the context-aware applications. Moreover, a cloud-based approach is introduced in our work to protect the user’s privacy from third parties. The obtained performance measures are compared with existing approaches in terms of privacy policy breaches, context sensitivity, satisfaction threshold, adversary power, and convergence speed for online and offline attacks.

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Section: Analysis Of Performance Metrics For the Proposed Modelmentioning

confidence: 99%

An adaptive approach for preserving privacy in context aware applications for smartphones in cloud computing platform

Gadiyar

G.S

Goudar

2023

Preprint

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“…Future work under progress is that we lay more focus on lightweight and dynamic searchable encryption schemes. In addition, we also plan to integrate federated learning [54], edge computing [55,56], and IoT [57] in this scenario to better enhance privacy protection.…”

Section: Summary and Future Workmentioning

confidence: 99%

Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT

Nie

Zhang

Chen

et al. 2022

Security and Communication Networks

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The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.

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“…For instance, edge computing strengthens from the intelligent decisions made by AI mechanisms, while AI models will be further improved by availing the distributed nature of edge computing. Yet, the edge server requires to collect all data generated by users for training and inference purposes, which might violate the user's privacy [13].…”

Section: Related Workmentioning

confidence: 99%

“…In doing so, each node independently selects a number (k) of desirable sub-datasets to be created, randomly picks k samples from the original data, and then assigns each one to each sub-dataset (lines 1-7). Afterward, and for each sample v in the remaining data, if v has not been assigned to any sub-dataset, the scheme calculates the similarity index of the said sample versus each sub-dataset (lines [13][14][15][16]. At the end of the process, the sample is assigned to the sub-dataset with the minimum similarity score.…”

Section: A Data-aware Splitting Schemementioning

confidence: 99%

Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks

Nour¹,

Cherkaoui²

2022

Preprint

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Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a datadriven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node's local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds.

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Privacy on the Edge: Customizable Privacy-Preserving Context Sharing in Hierarchical Edge Computing

Cited by 39 publications

References 25 publications

An adaptive approach for preserving privacy in context aware applications for smartphones in cloud computing platform

An adaptive approach for preserving privacy in context aware applications for smartphones in cloud computing platform

Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT

Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks

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