Privacy Protection Method Based on Access Control

2018 ◽  
Author(s):  
Xin Qiao ◽  
Lixiaoyang Wang ◽  
Bo Qin ◽  
Hong Chen ◽  
Suyun Zhao
Keyword(s):  
Access Control ◽  
Privacy Protection ◽  
Protection Method

A privacy protection method for health care big data management based on risk access control

2019 ◽  
Vol 23 (3) ◽  
pp. 427-442 ◽  
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Xiaohan Hu ◽  
Jingwei Shang
Keyword(s):  
Health Care ◽  
Big Data ◽  
Access Control ◽  
Data Management ◽  
Privacy Protection ◽  
Protection Method

scholarly journals Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

2018 ◽  
Vol 14 (11) ◽  
pp. 40
Author(s):  
Bohua Guo ◽  
Yanwu Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Random Number ◽  
Polynomial Regression ◽  
Homomorphic Encryption ◽  
Weighted Average ◽  
Base Station ◽  
Wireless Sensor ◽  
Protection Scheme ◽  
Protection Method

<p class="0abstract"><span lang="EN-US">To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.</span></p>

scholarly journals Coupling of Inference and Access Controls to Ensure Privacy Protection

2021 ◽  
Vol 11 (5) ◽  
pp. 529-535
Author(s):  
Jihane El Mokhtari ◽  
Anas Abou El Kalam ◽  
Siham Benhaddou ◽  
Jean-Philippe Leroy
Keyword(s):  
Access Control ◽  
Privacy Protection ◽  
Multidimensional Analysis ◽  
Sensitive Data ◽  
Inference Control ◽  
External Data ◽  
Control Rules ◽  
Access Controls ◽  
Policy Information ◽  
History Of

This article is devoted to the topic of coupling access and inference controls into security policies. The coupling of these two mechanisms is necessary to strengthen the protection of the privacy of complex systems users. Although the PrivOrBAC access control model covers several privacy protection requirements, the risk of inferring sensitive data may exist. Indeed, the accumulation of several pieces of data to which access is authorized can create an inference. This work proposes an inference control mechanism implemented through multidimensional analysis. This analysis will take into account several elements such as the history of access to the data that may create an inference, as well as their influence on the inference. The idea is that this mechanism delivers metrics that reflect the level of risk. These measures will be considered in the access control rules and will participate in the refusal or authorization decision with or without obligation. This is how the coupling of access and inference controls will be applied. The implementation of this coupling will be done via the multidimensional OLAP databases which will be requested by the Policy Information Point, the gateway brick of XACML to the various external data sources, which will route the inference measurements to the decision-making point.

Is It Privacy or Is It Access Control?

2014 ◽  
pp. 51-58 ◽  
Author(s):  
Sylvia L. Osborn
Keyword(s):  
Access Control ◽  
Privacy Protection ◽  
The Other ◽  
Data Sets ◽  
Online Systems ◽  
Other Hand ◽  
The One

With the widespread use of online systems, there is an increasing focus on maintaining the privacy of individuals and information about them. This is often referred to as a need for privacy protection. The author briefly examines definitions of privacy in this context, roughly delineating between keeping facts private and statistical privacy that deals with what can be inferred from data sets. Many of the mechanisms used to implement what is commonly thought of as access control are the same ones used to protect privacy. This chapter explores when this is not the case and, in general, the interplay between privacy and access control on the one hand and, on the other hand, the separation of these models from mechanisms for their implementation.

Blockchain-based Data Integrity and Privacy Protection Method for Swarm Robotics

2021 ◽  
pp. 698-707
Author(s):  
Bangyin Li ◽  
Yutao Chen ◽  
Zhiqiang Zuo ◽  
Jie Huang
Keyword(s):  
Privacy Protection ◽  
Swarm Robotics ◽  
Data Integrity ◽  
Protection Method
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