Supporting Model-Based Privacy Analysis by Exploiting Privacy Level Agreements

The rise of wireless applications based on RFID has brought up major concerns on privacy. Indeed nowadays, when such an application is deployed, informed customers yearn for guarantees that their privacy will not be threatened. One formal way to perform this task is to assess the privacy level of the RFID application with a model. However, if the chosen model does not reflect the assumptions and requirements of the analyzed application, it may misevaluate its privacy level. Therefore, selecting the most appropriate model among all the existing ones is not an easy task. This paper investigates the eight most well-known RFID privacy models and thoroughly examines their advantages and drawbacks in three steps. Firstly, five RFID authentication protocols are analyzed with these models. This discloses a main worry: although these protocols intuitively ensure different privacy levels, no model is able to accurately distinguish them. Secondly, these models are grouped according to their features (e.g., tag corruption ability). This classification reveals the most appropriate candidate model(s) to be used for a privacy analysis when one of these features is especially required. Furthermore, it points out that none of the models are comprehensive. Hence, some combinations of features may not match any model. Finally, the privacy properties of the eight models are compared in order to provide an overall view of their relations. This part highlights that no model globally outclasses the other ones. Considering the required properties of an application, the thorough study provided in this paper aims to assist system designers to choose the best suited model.

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Model-Based Privacy Analysis in Industrial Ecosystems

Modelling Foundations and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-61482-3_13 ◽

2017 ◽

pp. 215-231 ◽

Cited By ~ 12

Author(s):

Amir Shayan Ahmadian ◽

Daniel Strüber ◽

Volker Riediger ◽

Jan Jürjens

Keyword(s):

Model Based ◽

Industrial Ecosystems ◽

Privacy Analysis

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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