121,992 research outputs found
Formal analysis of BPMN via a translation into COWS
No full textA translation of the Business Process Modeling Notation into the process calculus COWS is presented. The stochastic extension of COWS is then exploited to address quantitative reasoning about the behaviour of business processes. An example of such reasoning is shown by running the PRISM probabilistic model checker on a case study
Preventing Information Inference in Access Control
No full textTechnological innovations like social networks, personal devices
and cloud computing, allow users to share and store online a huge
amount of personal data. Sharing personal data online raises significant
privacy concerns for users, who feel that they do not have
full control over their data. A solution often proposed to alleviate
users’ privacy concerns is to let them specify access control
policies that reflect their privacy constraints. However, existing approaches
to access control often produce policies which either are
too restrictive or allow the leakage of sensitive information. In this
paper, we present a novel access control model that reduces the risk
of information leakage. The model relies on a data model which
encodes the domain knowledge along with the semantic relations
between data. We illustrate how the access control model and the
reasoning over the data model can be automatically translated in
XACML.We evaluate and compare our model with existing access
control models with respect to its effectiveness in preventing leakage
of sensitive information and efficiency in authoring policies.
The evaluation shows that the proposed model allows the definition
of effective access control policies that mitigate the risks of
inference of sensitive data while reducing users’ effort in policy
authoring compared to existing models
Impact Analysis of Coordinated Cyber-Physical Attacks via Statistical Model Checking: A Case Study
No full textAn Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes
No full textMachine learning techniques applied to large and distributed data archives might result in the disclosure of sensitive information. Data often contain sensitive identifiable information, and even if these are protected, the excessive processing capabilities of current machine learning techniques might facilitate the identification of individuals. This discussion paper presents a decision-support framework for data anonymization. The latter relies on a novel approach that exploits data correlations, expressed in terms of relaxed functional dependencies (rfds), to identify data anonymization strategies for providing suitable trade-offs between privacy and data utility. It also permits to generate anonymization strategies leveraging multiple data correlations simultaneously to increase the utility of anonymized datasets. In addition, our framework provides support in the selection of the anonymization strategies by enabling an understanding of the trade-offs between privacy and data utility offered by the obtained strategies. Experiments on real-life datasets show that our approach achieves promising results in data utility while guaranteeing the desired privacy level. Additionally, it allows data owners to select anonymization strategies balancing their privacy and data utility requirements
An enhanced CFA for security policies
No full textWe introduce a Control Flow Analysis, improving the one in [6], that statically approximates the dynamic behaviour of mobile processes, expressed in (a variant of) the -calculus. Our analysis of a system is able to describe the behaviour of each sub-system, tracking where and between whom communications may occur. To identify each sub-system, we use a syntactic encoding of its position inside the abstract syntax tree
Privacy-aware web service composition and ranking
No full textService selection is a key issue in the Future Internet, where applications are built by composing services and content offered by different service providers. Most existing service selection schemas only focus on QoS properties of services such as throughput, latency and response time, or on their trust and reputation level. By contrast, the risk of privacy breaches arising from the selection of component services whose privacy policy is not compliant with customers' privacy preferences is largely ignored. In this paper, we propose a novel privacy-preserving Web service composition and selection approach which (i) makes it possible to verify the compliance between users' privacy requirements and providers' privacy policies and (ii) ranks the composite Web services with respect to the privacy level they offer. We demonstrate our approach using a travel agency Web service as an example of service composition
Engineering and verifying agent-oriented requirements augmented by business constraints with -Tropos
No full textWe propose B-Tropos as a modeling framework to support agent-oriented systems engineering, from high-level requirements elicitation down to execution-level tasks. In particular, we show how B-Tropos extends the Tropos methodology by means of declarative business constraints, inspired by the ConDec graphical language. We demonstrate the functioning of B-Tropos using a running example inspired by a real-world industrial scenario, and we describe how B-Tropos models can be automatically formalized in computational logic, discussing formal properties of the resulting framework and its verification capabilities
Purpose control : did you process the data for the intended purpose?
No full textData protection legislation requires personal data to be collected and processed only for lawful and legitimate purposes. Unfortunately, existing protection mechanisms are not appropriate for purpose control: they only prevent unauthorized actions from occurring and do not guarantee that the data are actually used for the intended purpose. In this paper, we present a flexible framework for purpose control, which connects the intended purpose of data to the business model of an organization and detects privacy infringements by determining whether the data have been processed only for the intended purpose
Survey on access control for community-centered collaborative systems
Full text linkThe last decades have seen a growing interest and demand for community-centered collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share, and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts toward the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control mechanisms typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems
A decision-support framework for data anonymization with application to machine learning processes
Full text linkThe application of machine learning techniques to large and distributed data archives might result in the disclosure of sensitive information about the data subjects. Data often contain sensitive identifiable information, and even if these are protected, the excessive processing capabilities of current machine learning techniques might facilitate the identification of individuals, raising privacy concerns. To this end, we propose a decision-support framework for data anonymization, which relies on a novel approach that exploits data correlations, expressed in terms of relaxed functional dependencies (RFDs) to identify data anonymization strategies providing suitable trade-offs between privacy and data utility. Moreover, we investigate how to generate anonymization strategies that leverage multiple data correlations simultaneously to increase the utility of anonymized datasets. In addition, our framework provides support in the selection of the anonymization strategy to apply by enabling an understanding of the trade-offs between privacy and data utility offered by the obtained strategies. Experiments on real-life datasets show that our approach achieves promising results in terms of data utility while guaranteeing the desired privacy level, and it allows data owners to select anonymization strategies balancing their privacy and data utility requirements
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