Wibson: A Case Study of a Decentralized, Privacy-Preserving Data Marketplace

2020 ◽  
pp. 149-170
Author(s):  
Matias Travizano ◽  
Carlos Sarraute ◽  
Mateusz Dolata ◽  
Aaron M. French ◽  
Horst Treiblmaier
Keyword(s):  
Privacy Preserving

scholarly journals Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 23
Author(s):  
Angeliki Kitsiou ◽  
Eleni Tzortzaki ◽  
Christos Kalloniatis ◽  
Stefanos Gritzalis
Keyword(s):  
Social Networks ◽  
Interdisciplinary Approach ◽  
Privacy Preserving ◽  
Social Characteristics ◽  
Behavioral Models ◽  
Social Landscape ◽  
Technological Context ◽  
Privacy Risks ◽  
Self Adaptive

Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

Framework Design and Case Study for Privacy-Preserving Medical Data Publishing

2013 ◽  
Vol 4 (4) ◽  
pp. 48-65 ◽  
Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang
Keyword(s):  
Data Sharing ◽  
Personal Information ◽  
Real Life ◽  
Privacy Preserving ◽  
Medical Data ◽  
Data Publishing ◽  
Free Text ◽  
Patient Privacy ◽  
Healthcare Data

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.

scholarly journals Differential Privacy for Stackelberg Games

2020 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Lesia Mitridati ◽  
Pascal Van Hentenryck
Keyword(s):  
Electricity Market ◽  
Original Problem ◽  
Differential Privacy ◽  
Stackelberg Game ◽  
Privacy Preserving ◽  
Coordination Mechanism ◽  
Stackelberg Games ◽  
Sensitive Information ◽  
Near Optimality

This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordination represents a classic Stackelberg game and relies on the exchange of sensitive information between the system agents. The paper is motivated by the observation that the perturbation introduced by traditional DP mechanisms fundamentally changes the underlying optimization problem and even leads to unsatisfiable instances. To remedy such limitation, the paper introduces the Privacy-Preserving Stackelberg Mechanism (PPSM), a framework that enforces the notions of feasibility and fidelity (i.e. near-optimality) of the privacy-preserving information to the original problem objective. PPSM complies with the notion of differential privacy and ensures that the outcomes of the privacy-preserving coordination mechanism are close-to-optimality for each agent. Experimental results on several gas and electricity market benchmarks based on a real case study demonstrate the effectiveness of the proposed approach. A full version of this paper [Fioretto et al., 2020b] contains complete proofs and additional discussion on the motivating application.

Framework Design and Case Study for Privacy-Preserving Medical Data Publishing

2015 ◽  
pp. 1115-1130
Author(s):  
Yu Niu ◽  
Ji-Jiang Yang ◽  
Qing Wang
Keyword(s):  
Data Sharing ◽  
Personal Information ◽  
Real Life ◽  
Privacy Preserving ◽  
Medical Data ◽  
Data Publishing ◽  
Patient Privacy ◽  
Multiple Sources ◽  
Healthcare Data

With the pervasive using of Electronic Medical Records (EMR) and telemedicine technologies, more and more digital healthcare data are accumulated from multiple sources. As healthcare data is valuable for both commercial and scientific research, the demand of sharing healthcare data has been growing rapidly. Nevertheless, health care data normally contains a large amount of personal information, and sharing them directly would bring huge threaten to the patient privacy. This paper proposes a privacy preserving framework for medical data sharing with the view of practical application. The framework focuses on three key issues of privacy protection during the data sharing, which are privacy definition/detection, privacy policy management, and privacy preserving data publishing. A case study for Chinese Electronic Medical Record (ERM) publishing with privacy preserving is implemented based on the proposed framework. Specific Chinese free text EMR segmentation, Protected Health Information (PHI) extraction, and K-anonymity PHI anonymous algorithms are proposed in each component. The real-life data from hospitals are used to evaluate the performance of the proposed framework and system.

Privacy Preserving Course Evaluations in Greek Higher Education Institutes: An e-Participation Case Study with the Empowerment of Attribute Based Credentials

2014 ◽  
pp. 140-156 ◽  
Author(s):  
Vasiliki Liagkou ◽  
George Metakides ◽  
Apostolis Pyrgelis ◽  
Christoforos Raptopoulos ◽  
Paul Spirakis ◽  
...  
Keyword(s):  
Higher Education ◽  
Privacy Preserving ◽  
Course Evaluations
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