Dynamic inference control in privacy preference enforcement

Author(s):  
Xiangdong An ◽  
Dawn Jutla ◽  
Nick Cercone
Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.


2021 ◽  
Vol 11 (5) ◽  
pp. 529-535
Author(s):  
Jihane El Mokhtari ◽  
Anas Abou El Kalam ◽  
Siham Benhaddou ◽  
Jean-Philippe Leroy

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.


2021 ◽  
Vol 16 (7) ◽  
pp. 2943-2964
Author(s):  
Xudong Lin ◽  
Xiaoli Huang ◽  
Shuilin Liu ◽  
Yulin Li ◽  
Hanyang Luo ◽  
...  

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.


2017 ◽  
Vol 70 ◽  
pp. 32-47 ◽  
Author(s):  
Joachim Biskup ◽  
Martin Bring ◽  
Michael Bulinski

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