JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Refinement of the General Data Protection Regulation (GDPR) Model: Administrative Fines Perspective

Isikuandmete kaitse üldmääruse (2016/679/EL; edaspidi ÜM) nõuetele vastamiseks vajavad organisatsioonid raamistikku, mis võimaldab hinnata oma äriprotsesside vastavust ÜM-ile. Sel eesmärgil on Tartu Ülikooli Arvutiteaduste Insituudi teadurid loomas tarkvaralist lahendust, mis võimaldab äriprotsesside vastavust ÜM-ile pool-automatiseerida. Lahenduse nimeks on hetkel pakutud Data Protection Observation Engine (edaspidi DPOE). Seni tehtud teadustöö on loonud DPOE kontseptuaalse mudeli, mis katab üldisi ÜM-i nõudeid UML formaadis kirjeldades peamisi olemeid, artefakte ja suhteid nende vahel (edaspidi DPOE Mudel). DPOE Mudel vajab aga valideerimist ÜM-i täielikkuse aspektist (st. kui palju ÜM-st on kaetud DPOE Mudeliga). Käesolev magistritöö täiendab olemasolevat teadustööd DPOE Mudeli õigusliku valideerimise näol. Valideerimine toimub ÜM artiklite 83(4) ja 83(5) baasil, mis kirjeldab võtmeartiklid, mille rikkumine võib kaasa tüüa rahatrahvid. Selline valideeriline võimaldab DPOE peamistel kasutajatel – andmekaitseametnikel – saada kindlust, et DPOE poolt genereeritud tulemused ja tõstatatud võimalikud mittevastavused on olulised, kuna need puudutavad võtmeartikleid. See omakorda tagab DPOE tulemuste terviklikkuse. Sellega luuakse ka võimalus võrrelda DPOE Mudeli hetkeversiooni täiustatud DPOE Mudeliga õigusliku täielikkuse (s.t. ÜM artiklite katmise) vaatest. DPOE Mudeli hetkeversiooni ja täiustatud versiooni rakendatakse äriprotsessile (ÕIS2 sisselogimine), et võrrelda, kui palju ÜM-i artikleid Mudelid katavad. Valideerimise ja mudelite rakendamisel äriprotsessile suurendatakse lõpptulemusena DPOE Mudeli küpsust.To meet the requirements of the General Data Protection Regulation (2016/679/EU; herein-after GDPR), organizations need a framework for assessing compliance of their business processes. For such purpose, a Data Protection Observation Engine (hereinafter DPOE) – a software tool enabling business process GDPR compliance check semi-automatically – is created by the researchers of Institute of Computer Science of University of Tartu. Current research on the DPOE has produced a conceptual model covering general GDPR require-ments in an UML format describing the key entities, artefacts and relationships between these (hereinafter DPOE Model). The DPOE Model, however, requires validation in terms of legal completeness (i.e. GDPR coverage). The thesis adds to the existing research by legally validating the DPOE Model from the perspective of Article 83(4) and 83(5) of the GDPR concerning administrative fines. These articles describe key GDPR requirements which’ infringement bring about fines up to 20,000,000 EUR. Thus, these are the require-ments every organization must treat with special attention in order to be compliant with the GDPR. This validation also enables the prime users of DPOE, the data protection officers, to trust the results generated by the DPOE as they know the potential incompliance issues raised are of key importance. This in turn ensures the integrity of the output of the DPOE. As such, the basis for comparing the current version of the DPOE Model to the refined DPOE Model in terms of legal completeness (i.e. GDPR article coverage) is created. In order to measure how legal completeness has in fact improved, the results generated by the refined DPOE Model are compared to the results generated by current version of the DPOE Model on an actual business process (ÕIS2 login process). As a result of the validation and the comparison of the current version of the Model to the refined Model, the maturity of the Model is enhanced

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review

In the realm of data protection, a striking disconnect prevails between traditional domains of doctrinal, legal, theoretical, and policy-based inquiries and a burgeoning body of empirical evidence. Much of the scholarly and regulatory discourse remains entrenched in abstract legal principles or normative frameworks, leaving the empirical landscape uncharted or minimally engaged. Since the birth of EU data protection law, a modest body of empirical evidence has been generated but remains widely scattered and unexamined. Such evidence offers vital insights into the perception, impact, clarity, and effects of data protection measures but languishes on the periphery, inadequately integrated into the broader conversation. To make a meaningful connection, we conduct a comprehensive review and synthesis of empirical research spanning nearly three decades (1995- March 2022), advocating for a more robust integration of empirical evidence into the evaluation and review of the GDPR, while laying a methodological foundation for future empirical research

A Meta-Model Driven Method for Establishing Business Process Compliance to GDPR

2016. aasta aprillis kiitis Euroopa Parlament ja Nõukogu heaks ning võttis vastu uue isikuandmete kaitse määruse - GDPRi (Isikuandmete kaitse üldmäärus), mis jõustub 2018. aasta mai lõpus Euroopa Liidus (EL). GDPRi eesmärgiks on lahendada ELi kodanike isikuandmete kaitse ja kasutamisega seotud päevakohaseid probleeme. Uue määruse kohaselt kõik organisatsioonid, mis kasutavad ELi kodanike isikuandmeid oma igapäevases tegevuses, peavad oma infosüsteeme ja äriprotsesse ümber hindama, et need vastaksid uutele eeskirjadele ja piirangutele. Isikuandmete väärkasutus võib ettevõttele olla väga kulukas - kuni 20 miljonit eurot või 4% aastasest käibest trahvidena. Sellele vaatamata puudub tehniline juhis või selge lähenemisviis, mis aitaks hinnata infosüsteemide äriprotsesside vastavust GDPRi nõuetele. Käesolev töö käsitleb mainitud probleemi, uurides üldmääruse õigusakti teksti ja pakkudes välja infosüsteemide äriprotsesside analüüsimise metoodikat, mis aitaks viia äriprotsesse vastavusse GDPRi nõuetele. Pakutud metoodika aitab kaardistada isikuandmete liikumist erinevate osapoolte vahel ja tuua välja äriprotsessi probleemsed kohad, mis aitab vähendada isikuandmete kuritarvitamist. Pakutud metoodikat saab kasutada ka automatiseeritud tööriista väljatöötamiseks.In the April 2016, the European Parliament and Council approved the new personal data protection regulation - GDPR (General Data Protection Regulation), which will take effect at the end of the May 2018 in all Member States of European Union (EU). The GDPR is addressing common problems of the protection and the usage of the personal data of EU citizens. According to the new regulation, all organizations that use personal data of EU citizens in their day-to-day activities - have to re-evaluate their business processes and information systems to comply with the new rules and constraints. The punishment for misuse of personal data can be very costly to the company - up to 20 million euros or 4% of the annual global turnover in fines. Nevertheless, there is no technical guidance or clear approach that would help to evaluate business processes of an information system to comply with GDPR. This thesis will address mentioned issue by researching the GDPR legislation text and proposing an actual methodology for analysing business processes of information systems and aligning them with the GDPR. The proposed methodology will also help to map the flow of the personal data between different parties and highlight the problematic places in the business processes suggesting measures to reduce the misuse of personal data. This approach could be used as a reference point for developing the automated tool for analysing the processes of an information system to comply with GDPR