RDF Graph Anonymization Robust to Data Linkage

Delanaux, Remy; Bonifati, Angela; Rousset, Marie-Christine; Thion, Romuald

doi:10.1007/978-3-030-34223-4_31

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…A query-based logical framework for RDF data has been introduced in [7,8], where sensitive information is expressed as SPARQL queries whose results must not disclose sensitive information of individual. It has been extended to handling utility queries in [4,5]. These approaches however are restricted to simple conjunctive queries.…”

Section: Related Workmentioning

confidence: 99%

Identifying Privacy Risks Raised by Utility Queries

Asghar

Bobineau

Rousset

2022

Web Information Systems Engineering – WISE 2022

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Personal data are increasingly disseminated over the Web through mobile devices and smart environments, and are exploited for developing more and more sophisticated services and applications. All these advances come with serious risks for privacy breaches that may reveal private information wanted to remain undisclosed by data producers. It is therefore of utmost importance to help them to identify privacy risks raised by requests of service providers for utility purposes. In this paper, we first formalize privacy risks by privacy queries expressed (and kept secret) by each data producer to specify the data they do not want to be disclosed. Then, we develop a formal approach for detecting incompatibility between privacy and utility queries expressed as temporal aggregate conjunctive queries. The distinguishing point of our approach is to be data-independent and to come with an explanation based on the query expressions only. This explanation is intended to help data producers understand the detected privacy breaches and guide their choice of the appropriate technique to correct it.

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Section: Related Workmentioning

confidence: 99%

Identifying Privacy Risks Raised by Utility Queries

Asghar

Bobineau

Rousset

2022

Web Information Systems Engineering – WISE 2022

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“…First, instead of making the data public, one can provide only restricted access through queries, whose answers are monitored by a "censor", which may decide not to give an answer or even lie if needed to satisfy the constraints [7][8][9]. Second, one can publish the data in an appropriately anonymized form, while keeping as much information about individuals as is allowed by the policy available [2,4,6,10,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The papers [2,4,6] and this one allow for arbitrary modifications of the original data set, as long as the new data is logically implied by the original one. In contrast, the work from [10,13,14] restricts modifications to the application of certain anonymization operations. Another distinguishing criterion is which formalisms are employed for representing the data and the policy.…”

Section: Introductionmentioning

confidence: 99%

“…Another distinguishing criterion is which formalisms are employed for representing the data and the policy. While in the work described in [10,13,14] RDF graphs are used to represent the data and conjunctive queries to describe the policy, the papers [2,4,6] consider the setting where DL ABoxes represent the data and concepts of the DL EL describe the policy. More precisely, a restricted form of ABoxes, called instance store, is considered in [2,6], whereas in [4] and in the present paper so-called quantified ABoxes are employed.…”

Section: Introductionmentioning

confidence: 99%

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Safety of quantified ABoxes w.r.t. singleton ε L policies

Baader

Kriegel

Nuradiansyah

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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In recent work, we have shown how to compute compliant anonymizations of quantified ABoxes w.r.t. EL policies. In this setting, quantified ABoxes can be used to publish information about individuals, some of which are anonymized. The policy is given by concepts of the Description Logic (DL) EL, and compliance means that one cannot derive from the ABox that some non-anonymized individual is an instance of a policy concept. If one assumes that a possible attacker could have additional knowledge about some of the involved non-anonymized individuals, then compliance with a policy is not sufficient. One wants to ensure that the quantified ABox is safe in the sense that none of the secret instance information is revealed, even if the attacker has additional compliant knowledge. In the present paper, we show that safety can be decided in polynomial time, and that the unique optimal safe anonymization of a non-safe quantified ABox can be computed in exponential time, provided that the policy consists of a single EL concept.

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“…In particular, this sort of attacks may occur when the combination of the repaired ontology with the background knowledge of the attackers still violates a privacy policy. Indeed, considering such malicious attacks for ontologies in the last step above has also been recently investigated in different contexts, such as in the area of linked data [6,9] or in the area of ontology-based data integration (OBDI) [4]. In the context of privacy in DL ontologies, to the best of our knowledge, the studies of preserving identity or reckoning such linkage attacks were still unexplored, whereas the studies of ontology repairs have been carried out by e.g., [10,16] with different settings and motivations.…”

Section: Introductionmentioning

confidence: 99%

Reasoning in Description Logic Ontologies for Privacy Management

Nuradiansyah

2020

Künstl Intell

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This work is initially motivated by a privacy scenario in which the confidential information about persons or its properties formulated in description logic (DL) ontologies should be kept hidden. We investigate procedures to detect whether this confidential information can be disclosed in a certain situation by using DL formalisms. If it is the case that this information can be deduced from the ontologies, which implies certain privacy policies are not fulfilled, then one needs to consider methods to repair these ontologies in a minimal way such that the modified ontologies complies with the policies. However, privacy compliance itself is not enough if a possible attacker can also obtain relevant information from other sources, which together with the modified ontologies might violate the privacy policy. This article provides a summary of studies and results from Adrian Nuradiansyah’s Ph.D. dissertation that are corresponding to the addressed problem above with a special emphasis on the investigations on the worst-case complexities of those problems as well as the complexity of the procedures and algorithms solving the problems.

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RDF Graph Anonymization Robust to Data Linkage

Cited by 7 publications

References 19 publications

Identifying Privacy Risks Raised by Utility Queries

Identifying Privacy Risks Raised by Utility Queries

Safety of quantified ABoxes w.r.t. singleton ε L policies

Reasoning in Description Logic Ontologies for Privacy Management

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