108,337 research outputs found

    A language for automatically enforcing privacy policies

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    It is becoming increasingly important for applications to protect sensitive data. With current techniques, the programmer bears the burden of ensuring that the application's behavior adheres to policies about where sensitive values may flow. Unfortunately, privacy policies are difficult to manage because their global nature requires coordinated reasoning and enforcement. To address this problem, we describe a programming model that makes the system responsible for ensuring adherence to privacy policies. The programming model has two components: 1) core programs describing functionality independent of privacy concerns and 2) declarative, decentralized policies controlling how sensitive values are disclosed. Each sensitive value encapsulates multiple views; policies describe which views are allowed based on the output context. The system is responsible for automatically ensuring that outputs are consistent with the policies. We have implemented this programming model in a new functional constraint language named Jeeves. In Jeeves, sensitive values are introduced as symbolic variables and policies correspond to constraints that are resolved at output channels. We have implemented Jeeves as a Scala library using an SMT solver as a model finder. In this paper we describe the dynamic and static semantics of Jeeves and the properties about policy enforcement that the semantics guarantees. We also describe our experience implementing a conference management system and a social network

    Semantic-Based Privacy Protection of Electronic Health Records for Collaborative Research

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    Combined health information and web-based technologies can be used to support healthcare and research activities associated with electronic health records (EHRs). EHRs used for research purposes demand privacy, confidentiality and all information governance concerns are addressed. However, existing solutions are unable to meet the evolving research needs especially when supporting data access and linkage across organization boundaries. In this work, we show how semantic methods can aid in the specification and enforcement of policies for privacy protection. This is illustrated through a case study associated with the Australasian Diabetes Data Network (ADDN), the national paediatric type-1 diabetes data registry and the Australian Urban Research Infrastructure Network (AURIN) platform that supports Australia-wide access to urban and built environment data sets. Specifically we show that through extending the eXtensible Access Control Markup Language (XACML) with semantic capabilities, we are able to support fine-grained privacy-preserving policies leveraging semantic reasoning that is not directly available in XACML or other existing security policy specification languages

    Mandatory Enforcement of Privacy Policies using Trusted Computing Principles

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    Modern communication systems and information technology create significant new threats to information privacy. In this paper, we discuss the need for proper privacy protection in cooperative intelligent transportation systems (cITS), one instance of such systems. We outline general principles for data protection and their legal basis and argue why pure legal protection is insufficient. Strong privacy-enhancing technologies need to be deployed in cITS to protect user data while it is generated and processed. As data minimization cannot always prevent the need for disclosing relevant personal information, we introduce the new concept of mandatory enforcement of privacy policies. This concept empowers users and data subjects to tightly couple their data with privacy policies and rely on the system to impose such policies onto any data processors. We also describe the PRECIOSA Privacy-enforcing Runtime Architecture that exemplifies our approach. Moreover, we show how an application can utilize this architecture by applying it to a pay as you drive (PAYD) car insurance scenario

    Privacy in an Ambient World

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    Privacy is a prime concern in today's information society. To protect\ud the privacy of individuals, enterprises must follow certain privacy practices, while\ud collecting or processing personal data. In this chapter we look at the setting where an\ud enterprise collects private data on its website, processes it inside the enterprise and\ud shares it with partner enterprises. In particular, we analyse three different privacy\ud systems that can be used in the different stages of this lifecycle. One of them is the\ud Audit Logic, recently introduced, which can be used to keep data private when it\ud travels across enterprise boundaries. We conclude with an analysis of the features\ud and shortcomings of these systems

    A Blockchain-based Approach for Data Accountability and Provenance Tracking

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    The recent approval of the General Data Protection Regulation (GDPR) imposes new data protection requirements on data controllers and processors with respect to the processing of European Union (EU) residents' data. These requirements consist of a single set of rules that have binding legal status and should be enforced in all EU member states. In light of these requirements, we propose in this paper the use of a blockchain-based approach to support data accountability and provenance tracking. Our approach relies on the use of publicly auditable contracts deployed in a blockchain that increase the transparency with respect to the access and usage of data. We identify and discuss three different models for our approach with different granularity and scalability requirements where contracts can be used to encode data usage policies and provenance tracking information in a privacy-friendly way. From these three models we designed, implemented, and evaluated a model where contracts are deployed by data subjects for each data controller, and a model where subjects join contracts deployed by data controllers in case they accept the data handling conditions. Our implementations show in practice the feasibility and limitations of contracts for the purposes identified in this paper

    Tell the Smart House to Mind its Own Business!: Maintaining Privacy and Security in the Era of Smart Devices

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    Consumers want convenience. That convenience often comes in the form of everyday smart devices that connect to the internet and assist with daily tasks. With the advancement of technology and the “Internet of Things” in recent years, convenience is at our fingertips more than ever before. Not only do consumers want convenience, they want to trust that their product is performing the task that they purchased it for and not exposing them to danger or risk. However, due to the increasing capabilities and capacities of smart devices, consumers are less likely to realize the implications of what they are agreeing to when they purchase and begin using these products. This Note will focus on the risks associated with smart devices, using smart home devices as an illustration. These devices have the ability to collect intimate details about the layout of the home and about those who live within it. The mere collection of this personal data opens consumers up to the risk of having their private information shared with unintended recipients whether the information is being sold to a third party or accessible to a hacker. Thus, to adequately protect consumers, it is imperative that they can fully consent to their data being collected, retained, and potentially distributed. This Note examines the law that is currently in place to protect consumers who use smart devices and argues that a void ultimately leaves consumers vulnerable. Current data privacy protection in the United States centers on the self-regulatory regime of “notice and choice.” This Note highlights how the self-regulatory notice-and-choice model fails to ensure sufficient protection for consumers who use smart devices and discusses the need for greater privacy protection in the era of the emerging Internet of Things. Ultimately, this Note proposes a state-level resolution and calls upon an exemplar state to experiment with privacy protection laws to determine the best way to regulate the Internet of Things

    Secure data sharing and processing in heterogeneous clouds

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    The extensive cloud adoption among the European Public Sector Players empowered them to own and operate a range of cloud infrastructures. These deployments vary both in the size and capabilities, as well as in the range of employed technologies and processes. The public sector, however, lacks the necessary technology to enable effective, interoperable and secure integration of a multitude of its computing clouds and services. In this work we focus on the federation of private clouds and the approaches that enable secure data sharing and processing among the collaborating infrastructures and services of public entities. We investigate the aspects of access control, data and security policy languages, as well as cryptographic approaches that enable fine-grained security and data processing in semi-trusted environments. We identify the main challenges and frame the future work that serve as an enabler of interoperability among heterogeneous infrastructures and services. Our goal is to enable both security and legal conformance as well as to facilitate transparency, privacy and effectivity of private cloud federations for the public sector needs. © 2015 The Authors
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