BOLD Vision 2020:Designing a vision for the future of Big Open Legal Data

The vision of openlaws.eu is to make access to justice easier for citizens, business- es and legal experts. For this purpose, an innovative legal information platform has been designed by the openlaws.eu project, considering the needs of various stakeholder groups as well as the latest developments in technology and our information society. Access to justice is a fundamental problem in the European Union. There are over 500 million citizens and over 21 million businesses who live, work and operate in 28 jurisdictions, written in 24 official languages. A common market cannot work without a legal system as a basis. Legal information is a public good and it is the duty of governments and the EU to inform citizens and business about the law. In a democracy and under the rule of law everybody should know legislation and case law – or at least have access to it. Legal tech is a new terms for new technology that can be applied to legal information in order to create better access and better understanding of the law. However, just because things can be done, does not mean automatically that they are done. Financial and organisational restrictions and the lack of competency can be a deal-breaker for innovation. Open data, open innovation and open source software can be a potential solution to this problem, especially when combined to one coherent ecosystem. openlaws.eu has developed a prototype platform upon these new open concepts. The application and implementation of some of the features of this innovative legal cloud service indicate where the road of “Big Open Legal Data” can lead us in the upcoming years. The project team envisages an environment, where a “social layer” is put on top of the existing “institutional layer”. Citizens, businesses and legal experts can actively collaborate on the basis of primary legislation and case law. Linked and aggregated legal data provide a solid basis. Such information can then be represented in traditional and more innovative ways. Text and data mining as well as legal intelligence help to process large amounts of legal information automatically, so that experts can focus on the more complicated questions. In the next five years more and more legal data will be opened up, not only because of the PSI Directive, but also because it is in the best interest of governments. As a result, we anticipate that more legal tech start-ups will emerge, as already happened during the past two years. They will apply innovative concepts and new technology on existing legal information and create better access to justice in the EU, in Member States and in the world

Information-flow Analysis of Hibernate Query Languages

Hibernate Query Language (HQL) provides a framework for mapping object-oriented domain models to traditional relational databases. In this context, existing information leakage analyses cannot be applied directly, due to the presence and interaction of high-level application variables and SQL database attributes. The paper extends the Abstract Interpretation framework to properly deal with this challenging applicative scenario, by using the symbolic domain of positive propositional formulae to capture variable dependences affecting (directly or indirectly) the propagation of confidential data

Information Leakage Analysis of Database Query Languages

In this work, we extend language-based information-flow security analysis to the case of database applications embedding query languages. The analysis is performed by (i) computing an overapproximation of variables’ dependences, in the form of propositional formula, occurred up to each program point, (ii) checking the satisfiability on assigning truth values to variables, (iii) analyzing the application over a numerical abstract domain, and finally, (iv) enhancing the analysis using the reduced product of the propositional formulae domain and the numerical abstract domain

Hypercollecting Semantics and its Application to Static Analysis of Information Flow

International audienceWe show how static analysis for secure information flow can be expressed and proved correct entirely within the framework of abstract interpretation. The key idea is to define a Galois connection that directly approximates the hyperproperty of interest. To enable use of such Galois connections, we introduce a fixpoint characterisation of hypercollecting semantics, i.e. a " set of sets " transformer. This makes it possible to systematically derive static analyses for hyper-properties entirely within the calculational framework of abstract interpretation. We evaluate this technique by deriving example static analyses. For qualitative information flow, we derive a dependence analysis similar to the logic of Amtoft and Banerjee (SAS'04) and the type system of Hunt and Sands (POPL'06). For quantitative information flow, we derive a novel cardinality analysis that bounds the leakage conveyed by a program instead of simply deciding whether it exists. This encompasses problems that are hypersafety but not k-safety. We put the framework to use and introduce variations that achieve precision rivalling the most recent and precise static analyses for information flow