PePPer: Fine-Grained Personal Access Control via Peer Probing

Amsterdamer, Yael; Drien, Osnat

doi:10.1109/icde.2019.00227

Cited by 4 publications

(4 citation statements)

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“…A collaborative access control tool is suggested in [38], where peers are actively probed to ask for their permissions. This tool is especially important when it is difficult to identify the data owner or when it is unknown in advance how the data will be processed.…”

Section: Background and Related Workmentioning

confidence: 99%

A Privacy Providing Context-based Trust Model for OSN and its Relation to GDPR

Voloch,

Gal-Oz,

Gudes

2023

Preprint

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Users’ privacy has become a key objective inthe operation of Online Social Networks (OSN), whichinvolves major concerns regarding personal data exposure and processing. It became a demand with the raiseof legislative initiatives such as the General Data Protection Regulation (GDPR) in the EU that affects mostof the commercial companies, government institutions,and other sectors that process personal data. While thesubject of ensuring privacy in OSN is widely addressedin current research, we focus on challenges related toseveral principles concerning the right to the protectionof personal data, as defined by the GDPR. In previousresearch we have devised a comprehensive Trust-basedModel for Social Networks, that uses Trust, Access Control and Flow Control, to enhance users’ control overtheir data. Users of social networks share data of various contexts, and for a specific user, some contexts aremore sensitive than others. In this paper we use contextdependent trust to build a refined model for controllingpersonal content that aligns with the users’ preferences.We propose a context-based trust model that involvesuser actions, OSN features and OSN activities. It provides a means for revealing the sources of sensitive datadissemination and prevents them from happening. Wevalidate this model by analyzing trust using sentimentanalysis of posts in a real network. The context-basedmodel we present in this paper, addresses the need toestablish an infrastructure for the enforcement of dataprotection rules, concerning users’ privacy in OSN, suchas data minimization and the right-to-be-forgotten. Wealso propose a solution for Digital Rights Management(DRM) to control and manage access to copyrightedmaterial.

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Section: Background and Related Workmentioning

confidence: 99%

A Privacy Providing Context-based Trust Model for OSN and its Relation to GDPR

Voloch,

Gal-Oz,

Gudes

2023

Preprint

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“…These include system testing, e.g., [8,44] (where it is called Sequential System Testing or Sequential Diagnosis), BDD design, e.g., [13,18,31] (where it is also called Discrete Function Evaluation), active learning [24] (where it is a particular case of Bayesian Active Learning) and its connection to other problems such as Stochastic Set Cover [15,29] (where it is termed Stochastic Boolean Function Evaluation (SBFE)). Boolean Evaluation is also a component of Consent Management [6,17], where the choice of variables to observe coincides with requests to attain consent to use specific input tuples. Many of these studies focus on optimization goals different than ours, most notably the expected number of observed variables under a probabilistic model [2,6,8,13,15,17,29,31,44], corresponding to the expected path length in a BDD; but other alternatives such as minimizing the size of the BDD have been considered [19].…”

Section: Related Workmentioning

confidence: 99%

“…Boolean Evaluation is also a component of Consent Management [6,17], where the choice of variables to observe coincides with requests to attain consent to use specific input tuples. Many of these studies focus on optimization goals different than ours, most notably the expected number of observed variables under a probabilistic model [2,6,8,13,15,17,29,31,44], corresponding to the expected path length in a BDD; but other alternatives such as minimizing the size of the BDD have been considered [19].…”

Section: Related Workmentioning

confidence: 99%

“…Interactive Boolean Evaluation has been extensively studied, and has mostly focused on the following problem: given probabilities on the truth values of the Boolean variables (namely, the probability of obtaining an affirmative probe answer for each variable), we must determine a probing strategy (i.e., which variables to probe and in what order) so that the expected number of probes is minimized. There is a significant body of work on the topic (e.g., [2,6,8,13,15,17,29,31,44]) and in [17] we have shown how to leverage this work for our application of consent management.…”

Section: Introductionmentioning

confidence: 99%

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Worst-case Analysis for Interactive Evaluation of Boolean Provenance

Amarilli¹,

Amsterdamer²

2022

Preprint

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In recent work, we have introduced a framework for fine-grained consent management in databases, which combines Boolean data provenance with the field of interactive Boolean evaluation. In turn, interactive Boolean evaluation aims at unveiling the underlying truth value of a Boolean expression by frugally probing the truth values of individual values. The required number of probes depends on the Boolean provenance structure and on the (a-priori unknown) probe answers. Prior work has analyzed and aimed to optimize the expected number of probes, where expectancy is with respect to a probability distribution over probe answers. This paper gives a novel worst-case analysis for the problem, inspired by the decision tree depth of Boolean functions.Specifically, we introduce a notion of evasive provenance expressions, namely expressions, where one may need to probe all variables in the worst case. We show that read-once expressions are evasive, and identify an additional class of expressions (acyclic monotone 2-DNF) for which evasiveness may be decided in PTIME. As for the more general question of finding the optimal strategy, we show that it is coNP-hard in general. We are still able to identify a sub-class of provenance expressions that is "far from evasive", namely, where an optimal worst-case strategy probes only O(log n) out of the n variables in the expression, and show that we can find this optimal strategy in polynomial time.

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