A Formal Model of Data Privacy

Stouppa, Phiniki; Studer, Thomas

doi:10.1007/978-3-540-70881-0_34

Cited by 19 publications

(8 citation statements)

References 22 publications

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“…Fourth, while many approaches employ an abstract notion of a system in terms of abstract traces or states, in contrast (but similar to, e.g., [27,34,41,42,44]), we deal with the particularities of logic-oriented information systems, where the "state" of a client is represented by sets of sentences of a logic, as stored in the log files, where the logic provides precise semantics of logical implication to express how those states might be related. Given these and further differences, so far we see the recently proposed, highly expressive model for secrecy in multiagent systems [27] as the only promising candidate to cast our model in a more comprehensive one, thereby opening the way for more systematic comparisons.…”

Section: Conclusion and Related Workmentioning

confidence: 97%

“…Accordingly, our contribution is in line with many other works on "possibilistic secrecy", a notion which in our specific case refers to the possibility (existence) of alternative "safe" instances together with a generating alternative interaction sequence, as precisely described in Definition 3. This line has a long tradition, already treated in [20] regarding early contributions, substantially pushed by studying various notions of "noninterference", see, e.g., [25,33,37], and more recently revived in, e.g., [27,41,42,44]. Often such work was extended to "probabilistic secrecy", see, e.g., [22,26,27,30,[34][35][36]39].…”

Section: Conclusion and Related Workmentioning

confidence: 99%

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Inference-proof view update transactions with forwarded refreshments

Biskup¹,

Gogolin

Seiler

et al. 2011

JCS

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Inference control aims at disabling a participant to gain a piece of information to be kept confidential. Considering a server-client architecture for information systems, we extend Controlled Query Evaluation (CQE), an inference control method to enforce confidentiality in static information systems under queries, to databases that are updatable by a client. More specifically, within the framework of the lying approach to CQE, we study how the server should translate a view update request issued by a client into a new database state in an inference-proof way. In order to avoid dangerous inferences, some such updates have to be denied even though the new database instance would be compatible with the set of integrity constraints declared in the schema and supposed to be known to the client. In contrast, seen from the client's point of view some other updates leading to an incompatible instance should not be denied. We design a control method to resolve this seemingly paradoxical situation and then prove that the general security definitions of CQE, suitably extended to capture both query evaluation and view update processing, and other properties linked to view updates hold. Moreover, we further enhance that control method by adding an inference-proof subprotocol for refreshing the views of the other clients. To ensure inferenceproofness, from the other clients' point of view, any view update might be a transaction, i.e., a sequence of elementary updates. 1

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Section: Conclusion and Related Workmentioning

confidence: 97%

Section: Conclusion and Related Workmentioning

confidence: 99%

Inference-proof view update transactions with forwarded refreshments

Biskup¹,

Gogolin

Seiler

et al. 2011

JCS

View full text Add to dashboard Cite

show abstract

“…Data privacy was studied for EL ontologies by Tao et al [21]. Bao et al introduced the notion of a privacy-preserving reasoner [22] and Stouppa et al proposed a framework for data privacy in the context of ALC ontologies [23]. Finally, Calvanese et al [24] proposed techniques for ontology access authorisation based on Zhang and Mendelzon's database authorisation views paradigm [15].…”

Section: Related Workmentioning

confidence: 99%

Controlled Query Evaluation over OWL 2 RL Ontologies

Grau

Kharlamov

Kostylev

et al. 2013

Advanced Information Systems Engineering

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We study confidentiality enforcement in ontology-based information systems where ontologies are expressed in OWL 2 RL, a profile of OWL 2 that is becoming increasingly popular in Semantic Web applications. We formalise a natural adaptation of the Controlled Query Evaluation (CQE) framework to ontologies. Our goal is to provide CQE algorithms that (i) ensure confidentiality of sensitive information; (ii) are efficiently implementable by means of RDF triple store technologies; and (iii) ensure maximality of the answers returned by the system to user queries (thus restricting access to information as little as possible). We formally show that these requirements are in conflict and cannot be satisfied without imposing restrictions on ontologies. We propose a fragment of OWL 2 RL for which all three requirements can be satisfied. For the identified fragment, we design a CQE algorithm that has the same computational complexity as standard query answering and can be implemented by relying on state-of-the-art triple stores.

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“…In this case, various access rights are provided to users, and the privacy preservation implies that the users can only retrieve the information they are allowed to access either directly or indirectly by way of logical inference. The privacy of data in the systems of information is a high area of research, which is active particularly in the case of the databases (DBs) (Stouppa & Studer, 2007). Existing works on the privacy of data in databases aim mainly at the complete relational DBs (Biskup & Bonatti, 2004;VitoRacanelli et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Ontology and crow optimization-based deep belief network for privacy preservation of medical data

Jose¹,

Poulose²

2021

JER is an international, peer-reviewed journal that publishes f

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Medical data classification is used to find the hidden patterns of data by training a large amount of patient data collected from the providers. As the medical data is very sensitive, it must be a safeguard from all the noncollaborative means. Thus, it is important to take steps to preserve the confidential medical data. Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically. This classifier works based on three phases, including generation of the privacy-preserved data, construction of ontology, and classification. The Deep convolutional kernel approach is used to provide data confidentiality using the optimal coefficients. The construction of ontology is done with the cardiac heart disease terms used in the medical field for classification. Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA). The performance is analyzed in terms of the metrics, namely, accuracy, fitness, sensitivity, and specificity. The proposed CS-DBN method produces higher fitness, accuracy, sensitivity, and specificity of 0.9007, 0.8842, 1, and 0.8408, respectively.

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A Formal Model of Data Privacy

Cited by 19 publications

References 22 publications

Inference-proof view update transactions with forwarded refreshments

Inference-proof view update transactions with forwarded refreshments

Controlled Query Evaluation over OWL 2 RL Ontologies

Ontology and crow optimization-based deep belief network for privacy preservation of medical data

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