Multi-level Policy-aware Privacy Analysis

Projekt NAPLES (Novel Tools for Analysing Privacy Leakages – Privaatslekete Analüüsi Uudsed Vahendid) on Tartu Ülikooli ja Cybernetica AS-i ühine teadusprojekt, mida rahastab Kaitsealase Täiustatud Uurimisprojektide Agentuuri (DARPA) Brandeisi programm.NAPLES-i raames on välja töötatud teooria ja erinevaid tööriistu, et tuvastada ning kirjeldada infosüsteemide andmelekkeid. PLEAK on tööriist, mille sisendiks on äriprotsessimudeli ja -notatsiooni (BPMN) abil kirja pandud äriprotsess. Lisaks standardsele notatsioonile on mudelile lisatud arvutuslikke detaile ning infot privaatsuskaitse tehnoloogiate kohta, mis võimaldavad erinevatel tasemetel privaatsuslekete analüüse. NAPLES-i projekti käigus on loodud mitu erinevat analüüsitööriista. Peamiselt keskenduvad analüsaatorid niinimetatud "SQL koostöövoole" - BPMN-i koostöö mudelile, mille tegevused ning andmeobjektid on kirjeldatud vastavalt SQL päringute ning tabeli skeemidega. Binaarne avalikustamise analüüs annab privaatsuskaitse tehnoloogiate põhjal kõrgtasemelise ülevaate selle kohta, kellele on mingid andmed kättesaadavad. Teised analüüsivahendid nagu Leaks-When (Millal lekib) ja Guessing Advantage (äraarvamise edukus) lisavad detailsemad kvalitatiivseid ning kvantitatiivseid meetmeid lekete paremaks mõistmiseks.Minu töö oli NAPLE projekti osa ning minu panused olid mitmesugused.Esiteks ma lisasin globaalse ja lokaalse privaatsuspoliitika ideed SQL koostöövoogudessse. Privaatsuspoliitika tagab äriprotsessis osalejale ligipääsuõiguse mingile osale SQL skeemiga kirjeldatud andmetest. Teiseks ma kavandasin ning integreerisin mitmekihilise lekkanalüüsi alates binaarsest avalikustamise analüüsist (millised andmed on nähtaval) kuni tingimusliku avalikustamise (mis tingimustel leke toimub) ja kvantitatiivse meetmeni (kui palju andmete kohta lekib). Mitmekihiline analüüs põhineb PLEAK-i analüsaatoritel, kuid neid oli vaja täiendada, et nad toetaksid ühtseid sisendeid ning et Leaks-When ja Guessing Advantage tööriistad põhineksid privaatsuspoliitikatel. Lisaks arendasin juhtumiuuringu, et demonstreerida integreeritud mitmetasandilist privaatsusanalüüsi ning PLEAK-i tööriistu.The NAPLES (Novel Tools for Analysing Privacy Leakages) project is a research initiative conducted as a collaboration between Cybernetica AS and the University of Tartu, with funds of the Brandeis program of the Defense Advanced Research Projects Agency (DARPA). The research project has produced the theory and a set of tools for the analysis of privacy-related concerns, to determine the potential leakage of the data from the information systems. Specifically, PLEAK is a tool that takes as input business processes specified with the Business Process Model and Notation (BPMN), where modelentities are associated with privacy-enhancing technologies, in order to enable the analysis of privacy concerns at different levels of granularity. With the time, the NAPLES project has produced several analyzers. Such analyzers target SQLcollaborative workflows, that is, BPMN collaborative models that specify the steps of computation that correspond to SQL manipulation statements over the data objects representing the SQL data sources. The simple disclosure analysis performs a high-level data reachability analysis that reveals potentialdata leakages in the privacy-enhanced model of a business process: it tells whether a data object is visible to a given party. Other analyzers, such as the Leaks-When and the Guessing Advantage ones, provide finer-grained, qualitative and quantitative measures of data leakage to stakeholders.My work was part of the NAPLES project and my contributions are manifold. First, I added the concept of Global and Local privacy policies in the SQL collaborative workflows, which endow a party of the business process with access rights to the selected SQL entities with defined constraints. Second,I designed an integrated multi-level approach to the disclosure analysis: from the high-level declarative disclosure (What data might leak?) to the conditional disclosure (When does data leak?) and quantitative measure (How much does data leak?). This approach is based on existing tools of PLEAK for privacyanalysis. However, I refined these tools to accept more unified set of inputs and integrated the privacy policies with the Leaks-When and Guessing Advantage analyzers. Finally, I developed a case study, which has been used for showcasing the aforementioned integrated multi-level approach to the disclosure analysis, and that has been used as a proof-of-concept for NAPLES tools

Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities

The communications and collaborations among activities, pro- cesses, or systems, in general, are the base of complex sys- tems defined as distributed systems. Given the increasing complexity of their structure, interactions, and functionali- ties, many research areas are interested in providing mod- elling techniques and verification capabilities to guarantee their correctness and satisfaction of properties. In particular, the formal methods community provides robust verification techniques to prove system properties. However, most ap- proaches rely on manually designed formal models, making the analysis process challenging because it requires an expert in the field. On the other hand, the BPM community pro- vides a widely used graphical notation (i.e., BPMN) to design internal behaviour and interactions of complex distributed systems that can be enhanced with additional features (e.g., privacy technologies). Furthermore, BPM uses process min- ing techniques to automatically discover these models from events observation. However, verifying properties and ex- pected behaviour, especially in collaborations, still needs a solid methodology. This thesis aims at exploiting the features of the formal meth- ods and BPM communities to provide approaches that en- able formal verification over distributed systems. In this con- text, we propose two approaches. The modelling-based ap- proach starts from BPMN models and produces process al- gebra specifications to enable formal verification of system properties, including privacy-related ones. The process mining- based approach starts from logs observations to automati- xv cally generate process algebra specifications to enable veri- fication capabilities