References of "Biczók, Gergely"
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See detailTogether or Alone: The Price of Privacy in Collaborative Learinig
Pejo, Balazs UL; Tang, Qiang; Biczók, Gergely

in Proceedings on Privacy Enhancing Technologies (2019, July)

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training ... [more ▼]

Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium. [less ▲]

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See detailTowards Systematic Specification of Non-Functional Requirements for Sharing Economy Services
Symeonidis, Iraklis UL; Schroers, Jessica; Mustafa A. Mustafa, Mustafa A. et al

in Towards Systematic Specification of Non-Functional Requirements for Sharing Economy Services (2019, May)

Sharing Economy (SE) systems use technologies to enable sharing of physical assets and services among individuals. This allows optimisation of resources, thus contributing to the re-use principle of ... [more ▼]

Sharing Economy (SE) systems use technologies to enable sharing of physical assets and services among individuals. This allows optimisation of resources, thus contributing to the re-use principle of Circular Economy. In this paper, we assess existing SE services and identify their challenges in areas that are not technically connected to their core functionality but are essential in creating trust: information security and privacy, personal data protection and fair economic incentives. Existing frameworks for elicitation of non-functional requirements are heterogeneous in their focus and domain specific. Hence, we propose to develop a holistic methodology for non-functional requirements specification for SE systems following a top-down-top approach. A holistic methodology considering non-functional requirements is essential and can assist in the analysis and design of SE systems in a systematic and unified way applied from the early stages of the system development. [less ▲]

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See detailCollateral damage of Facebook third-party applications: a comprehensive study
Symeonidis, Iraklis UL; Biczók, Gergely; Shirazi, Fatemeh et al

in Computers and Security (2018), 77

Third-party applications on Facebook can collect personal data of the users who install them, but also of their friends. This raises serious privacy issues as these friends are not notified by the ... [more ▼]

Third-party applications on Facebook can collect personal data of the users who install them, but also of their friends. This raises serious privacy issues as these friends are not notified by the applications nor by Facebook and they have not given consent. This paper presents a detailed multi-faceted study on the collateral information collection of the applications on Facebook. To investigate the views of the users, we designed a questionnaire and collected the responses of 114 participants. The results show that participants are concerned about the collateral information collection and in particular about the lack of notification and of mechanisms to control the data collection. Based on real data, we compute the likelihood of collateral information collection affecting users: we show that the probability is significant and greater than 80% for popular applications such as TripAdvisor. We also demonstrate that a substantial amount of profile data can be collected by applications, which enables application providers to profile users. To investigate whether collateral information collection is an issue to users’ privacy we analysed the legal framework in light of the General Data Protection Regulation. We provide a detailed analysis of the entities involved and investigate which entity is accountable for the collateral information collection. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency toward collateral information collection. Furthermore, we discuss alternative solutions highlighting other countermeasures such as notification and access control mechanisms, cryptographic solutions and application auditing. To the best of our knowledge this is the first work that provides a detailed multi-faceted study of this problem and that analyses the threat of user profiling by application providers. [less ▲]

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See detailCollateral Damage of Facebook Apps: Friends, Providers, and Privacy Interdependence
Symeonidis, Iraklis UL; Shirazi, Fatemeh; Biczók, Gergely et al

in Symeonidis, Iraklis (Ed.) ICT Systems Security and Privacy Protection (2016)

Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user's friends can collect and potentially ... [more ▼]

Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user's friends can collect and potentially misuse her personal data inflicting collateral damage on the user while leaving her without proper means of control. In this paper, we present a multi-faceted study on the collateral information collection of apps in social networks. We conduct a user survey and show that Facebook users are concerned about this issue and the lack of mechanisms to control it. Based on real data, we compute the likelihood of collateral information collection affecting users; we show that the probability is significant and depends on both the friendship network and the popularity of the app. We also show its significance by computing the proportion of exposed user attributes including the case of profiling, when several apps are offered by the same provider. Finally, we propose a privacy dashboard concept enabling users to control the collateral damage. [less ▲]

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