SPrivAD: A Secure and Privacy-Preserving Mutually Dependent Authentication and Data Access Scheme for Smart Communities

2022 ◽  
pp. 102610
Author(s):  
Abubakar Sadiq Sani ◽  
Elisa Bertino ◽  
Dong Yuan ◽  
Ke Meng ◽  
Zhao Yang Dong
Keyword(s):  
Data Access ◽  
Privacy Preserving ◽  
Access Scheme ◽  
Smart Communities

Privacy preserving data access scheme for IoT devices

2017 ◽  
Author(s):  
Mosarrat Jahan ◽  
Suranga Seneviratne ◽  
Ben Chu ◽  
Aruna Seneviratne ◽  
Sanjay Jha
Keyword(s):  
Data Access ◽  
Privacy Preserving ◽  
Access Scheme ◽  
Iot Devices

scholarly journals Privacy preserving cloud data access with multi-authorities

2013 ◽  
Author(s):  
Taeho Jung ◽  
Xiang-Yang Li ◽  
Zhiguo Wan ◽  
Meng Wan
Keyword(s):  
Data Access ◽  
Privacy Preserving ◽  
Cloud Data

scholarly journals Study of privacy-preserving framework for cloud storage

2011 ◽  
Vol 8 (3) ◽  
pp. 801-819 ◽  
Author(s):  
Huang Ruwei ◽  
Gui Xiaolin ◽  
Yu Si ◽  
Zhuang Wei
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Security Analysis ◽  
Bloom Filter ◽  
Data Access ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Index Structure ◽  
Structure Generation ◽  
Access Right

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

FW-DAS: Fast Wireless Data Access Scheme in Mobile Networks

2014 ◽  
Vol 13 (8) ◽  
pp. 4260-4272 ◽  
Author(s):  
Giwon Lee ◽  
Insun Jang ◽  
Sangheon Pack ◽  
Xuemin Shen
Keyword(s):  
Mobile Networks ◽  
Data Access ◽  
Wireless Data ◽  
Access Scheme

scholarly journals A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2109
Author(s):  
Liming Fang ◽  
Minghui Li ◽  
Lu Zhou ◽  
Hanyi Zhang ◽  
Chunpeng Ge
Keyword(s):  
Access Control ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Data Access ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Fine Grained ◽  
Data Access Control ◽  
Smart Watch ◽  
Performance And Evaluation

A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.

scholarly journals A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Yeong-Cherng Hsu ◽  
Chih-Hsin Hsueh ◽  
Ja-Ling Wu
Keyword(s):  
Data Privacy ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Data Access ◽  
Privacy Preserving ◽  
Secret Key ◽  
K Nearest Neighbor ◽  
Sensitive Data ◽  
Cloud Data ◽  
Cloud Server

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

scholarly journals Privacy Preservation of Healthcare Data in Hybrid Cloud using a Hybrid Meta-Heuristics Based Sanitization Technique

2019 ◽  
Vol 8 (4) ◽  
pp. 2882-2890
Keyword(s):  
Side Effects ◽  
Search Algorithm ◽  
Cloud Model ◽  
Gravitational Search Algorithm ◽  
Data Access ◽  
Privacy Preserving ◽  
Cloud Services ◽  
Sensitive Information ◽  
Hybrid Cloud ◽  
Healthcare Data

Over the recent years, the expansion of cloud computing services enable hospitals and institutions to transit their healthcare data to the cloud, thus it provides the worldwide data access and on-demand high quality services at a cheaper rate. Despite the benefits of healthcare cloud services, the associated privacy issues are widely concerned by individuals and governments. Privacy risks rise when outsourcing personal healthcare records to cloud due to the sensitive nature of health information and the social and legal implications for its disclosure. Over the recent years, a privacy-preserving data mining (PPDM) technique has become a critical issue for the problems. Our goal is to design a privacy-preserving outsourcing framework under the hybrid cloud model. In this work we propose a Hybrid Ant Colony Optimization and Gravitational Search Algorithm (ACOGSA) to express the problem of hiding sensitive data through transaction deletion. Thus, it reduces the side effects of the hybrid cloud. Substantive experiments will be carried to compare the performance of the designed algorithm with the state-of-the-art approaches in terms of the side effects and database similarity (integrity). Over the past to sanitize the databases used for hiding sensitive information, a few heuristic approaches have been proposed. The method used for the comparison involves GA, PSO, ACO, and Firefly framework.

scholarly journals Federated learning for privacy-preserving data access

2021 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Małgorzata Śmietanka ◽  
Hirsh Pithadia ◽  
Philip Treleaven
Keyword(s):  
Data Access ◽  
Privacy Preserving
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