My Data, Your Data, Our Data: Managing Privacy Preferences in Multiple Subjects Personal Data

Gnesi, Stefania; Matteucci, Ilaria; Moiso, Corrado; Mori, Paolo; Petrocchi, Marinella; Vescovi, Michele

doi:10.1007/978-3-319-06749-0_11

Cited by 14 publications

(16 citation statements)

References 18 publications

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“…The adversary, typically an online service provider (or an external observer who has access to this information, e.g., another user of the social network), has access to the observed locations and co-locations of one or several users and seeks to locate users, at a given time instant, namely, carry out a localization attack 3 . Because of the co-location 2.…”

Section: Adversarymentioning

confidence: 99%

See 1 more Smart Citation

Quantifying Interdependent Privacy Risks with Location Data

Olteanu

Huguenin

Shokri

et al. 2017

IEEE Trans. on Mobile Comput.

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Abstract-Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users' IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users' locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

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Section: Adversarymentioning

confidence: 99%

“…Similarly, users who connect from the same IP address are likely to be attached to the same Internet access point, thus providing evidence of their co-location. Such data falls into the category of multiple-subjects personal data [3].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Interdependent Privacy Risks with Location Data

Olteanu

Huguenin

Shokri

et al. 2017

IEEE Trans. on Mobile Comput.

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“…Very limited individual capability to manage one's own data (Tene and Wolf, 2014;Gnesi et al, 2014) The framework shall guarantee the individual's right to manage and control personal data.…”

Section: Management Of Individual Datamentioning

confidence: 99%

“…Low level of individual expertise to analyze and manage complex privacy policies (Tene and Wolf, 2014;Gnesi et al, 2014) The framework shall provide comprehensible policies (Gnesi et al, 2014). management during 2016-2018.…”

Section: Privacy Policiesmentioning

confidence: 99%

Consent Management Architecture for Secure Data Transactions

Hyysalo

Hirvonsalo

Sauvola

et al. 2016

Proceedings of the 11th International Joint Conference on Software Technologies

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Abstract:Digitalization of data intensive services presents several challenges, such as how to safely manage and use the multitude of personal data across various public, private and commercial service providers. Guaranteed privacy is especially critical in sensitive cases like health data management and processing. A key challenge and enabler for efficient data utilization is the need for an adequate consent management framework that meets the General Data Protection Regulation (GDPR). To facilitate sensitive secure data transactions where end-control always resides with the individual, a consent management architecture (CMA) is defined, utilizing the new MyData approach. The proposed CMA enables context-driven authorization of multisourced data for safe access by various health services. CMA proof-of-concept and experiences are described and discussed to concretize and evaluate the suggested architecture. Consent management and authorization topics are discussed as a service function of the MyData Operator. The technical APIs required for registering and authorizing data sources and data services via the Operator are demonstrated and analyzed to expedite development of this important area within the research and industrial communities.

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“…The standard unraveling argument holds that giving up personal and private information also affects the privacy of other individuals as these are interdependent problems (so-called "privacy externality"). The inter-dependent information relating to multiple transaction parties as been termed "multiple subjects' personal data" (Gnesi et al 2014).…”

Section: Information Externalitiesmentioning

confidence: 99%

State-of-the-Art of the Economics of Cyber-Security and Privacy

Jentzsch

2016

SSRN Journal

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Box 1 Challenges in Estimation of Risk Metrics __________________________________________ Box 2 Important Standards in the PACS Domain ________________________________________ Box 3 The Economics of Big Data Analysis _____________________________________________ Box 4 Information Externalities and Pricing ____________________________________________ IPACSO-A Coordination Action under the FP7 DG CNECT Trustworthy ICT Program © IPACSO 2 IPACSO D4.1-p. 5 Preface IPACSO is an EU-funded CoOrdination and Support Action (CSA) project aimed at supporting Privacy and Cyber-security innovations in Europe. The key aim is to support ICT Security innovators with state of the art innovation methodologies and best practices that improve their overall innovation process. IPACSO will focus on adapting existing innovation methodologies available in other domains, both general and specific, optimizing these approaches for the Privacy and Cyber-Security (PACS) market domains. Ultimately, IPACSO will combine innovation support modules based on establish methods (both generic and technology-specific), with new innovation support approaches geared toward the specific needs of the European PACS marketplace. Market information of high-relevance to PACS innovators will also be included in the project. From an outreach perspective, innovators working alongside IPACSO will be able to increase their understanding of existing methodologies, best practices, market considerations, economic incentives and share key opinions alongside other peer experts in the PACS domain. By reaching out to IPACSO, privacy and cyber-security innovators will be put in touch with other innovators, business support organizations and financing. With IPACSO, privacy and cyber-security innovators will be getting additional promotion for their innovations, as IPACSO specifically focuses on innovations. The following scheme outlines in brief the relation of the two deliverables D4.1 and D4.2 to the developed Privacy and Cyber-Security Market Scheme. It enables the reader to see what is covered in the different deliverables and to what deliverable he/she needs to turn in order to find the information of interest. Cyber-Security Market Scheme and IPACSO Deliverable IPACSO-A Coordination Action under the FP7 DG CNECT Trustworthy ICT Program © IPACSO 2 IPACSO D4.1-p. 6

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My Data, Your Data, Our Data: Managing Privacy Preferences in Multiple Subjects Personal Data

Cited by 14 publications

References 18 publications

Quantifying Interdependent Privacy Risks with Location Data

Quantifying Interdependent Privacy Risks with Location Data

Consent Management Architecture for Secure Data Transactions

State-of-the-Art of the Economics of Cyber-Security and Privacy

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