A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements

Semantics-based Privacy by Design for Internet of Things Applications

As Internet of Things (IoT) technologies become more widespread in everyday life, privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers' questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Prot\'eg\'e HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58\% of privacy-related questions from software engineers

Semantics-based privacy by design for Internet of Things applications

As Internet of Things (IoT) technologies become more widespread in everyday life, privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers’ questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Protégé HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58% of privacy-related questions from software engineers

Viiteraamistik turvariskide haldamiseks plokiahela abil

Turvalise tarkvara loomiseks on olemas erinevad programmid (nt OWASP), ohumudelid (nt STRIDE), turvariskide juhtimise mudelid (nt ISSRM) ja eeskirjad (nt GDPR). Turvaohud aga arenevad pidevalt, sest traditsiooniline tehnoloogiline infrastruktuur ei rakenda turvameetmeid kavandatult. Blockchain näib leevendavat traditsiooniliste rakenduste turvaohte. Kuigi plokiahelapõhiseid rakendusi peetakse vähem haavatavateks, ei saanud need erinevate turvaohtude eest kaitsmise hõbekuuliks. Lisaks areneb plokiahela domeen pidevalt, pakkudes uusi tehnikaid ja sageli vahetatavaid disainikontseptsioone, mille tulemuseks on kontseptuaalne ebaselgus ja segadus turvaohtude tõhusal käsitlemisel. Üldiselt käsitleme traditsiooniliste rakenduste TJ-e probleemi, kasutades vastumeetmena plokiahelat ja plokiahelapõhiste rakenduste TJ-t. Alustuseks uurime, kuidas plokiahel leevendab traditsiooniliste rakenduste turvaohte, ja tulemuseks on plokiahelapõhine võrdlusmudel (PV), mis järgib TJ-e domeenimudelit. Järgmisena esitleme PV-it kontseptualiseerimisega alusontoloogiana kõrgema taseme võrdlusontoloogiat (ULRO). Pakume ULRO kahte eksemplari. Esimene eksemplar sisaldab Cordat, kui lubatud plokiahelat ja finantsjuhtumit. Teine eksemplar sisaldab lubadeta plokiahelate komponente ja tervishoiu juhtumit. Mõlemad ontoloogiaesitlused aitavad traditsiooniliste ja plokiahelapõhiste rakenduste TJ-es. Lisaks koostasime veebipõhise ontoloogia parsimise tööriista OwlParser. Kaastööde tulemusel loodi ontoloogiapõhine turberaamistik turvariskide haldamiseks plokiahela abil. Raamistik on dünaamiline, toetab TJ-e iteratiivset protsessi ja potentsiaalselt vähendab traditsiooniliste ja plokiahelapõhiste rakenduste turbeohte.Various programs (e.g., OWASP), threat models (e.g., STRIDE), security risk management models (e.g., ISSRM), and regulations (e.g., GDPR) exist to communicate and reduce the security threats to build secure software. However, security threats continuously evolve because the traditional technology infrastructure does not implement security measures by design. Blockchain is appearing to mitigate traditional applications’ security threats. Although blockchain-based applications are considered less vulnerable, they did not become the silver bullet for securing against different security threats. Moreover, the blockchain domain is constantly evolving, providing new techniques and often interchangeable design concepts, resulting in conceptual ambiguity and confusion in treating security threats effectively. Overall, we address the problem of traditional applications’ SRM using blockchain as a countermeasure and the SRM of blockchain-based applications. We start by surveying how blockchain mitigates the security threats of traditional applications, and the outcome is a blockchain-based reference model (BbRM) that adheres to the SRM domain model. Next, we present an upper-level reference ontology (ULRO) as a foundation ontology and provide two instantiations of the ULRO. The first instantiation includes Corda as a permissioned blockchain and the financial case. The second instantiation includes the permissionless blockchain components and the healthcare case. Both ontology representations help in the SRM of traditional and blockchain-based applications. Furthermore, we built a web-based ontology parsing tool, OwlParser. Contributions resulted in an ontology-based security reference framework for managing security risks using blockchain. The framework is dynamic, supports the iterative process of SRM, and potentially lessens the security threats of traditional and blockchain-based applications.https://www.ester.ee/record=b551352

A Data Scope Management Service to Support Privacy by Design and GDPR Compliance

In order to empower user data protection and user rights, the European General Data Protection Regulation (GDPR) has been enforced. On the positive side, the user is obtaining advantages from GDPR. However, organisations are facing many difficulties in interpreting GDPR, and to properly applying it, and, in the meanwhile, due to their lack of compliance, many organisations are receiving huge fines from authorities. An important challenge is compliance with the Privacy by Design and by default (PbD) principles, which require that data protection is integrated into processing activities and business practices from the design stage. Recently, the European Data Protection Board (EDPB) released an official document with PbD guidelines, and there are various efforts to provide approaches to support these. However, organizations are still facing difficulties in identifying a flow for executing, in a coherent, linear and effective way, these activities, and a complete toolkit for supporting this. In this paper, we propose the design of such flow, and our comprehensive supporting toolkit, as part of the DEFeND EU Project platform. Within DEFeND, we identified candidate tools, fulfilling specific GDPR aspects, and integrated them in a comprehensive toolkit: the DEFeND Data Scope Management service (DSM). The aim of DSM is to support organizations for continuous GDPR compliance through model-based Privacy by Design analysis. Here, we present DSM, its design, flow, and a preliminary case study and evaluation performed with pilots from the healthcare, banking, public administration and energy sectors

Anonymization Techniques for Privacy-preserving Process Mining

Process Mining ermöglicht die Analyse von Event Logs. Jede Aktivität ist durch ein Event in einem Trace recorded, welcher jeweils einer Prozessinstanz entspricht. Traces können sensible Daten, z.B. über Patienten enthalten. Diese Dissertation adressiert Datenschutzrisiken für Trace Daten und Process Mining. Durch eine empirische Studie zum Re-Identifikations Risiko in öffentlichen Event Logs wird die hohe Gefahr aufgezeigt, aber auch weitere Risiken sind von Bedeutung. Anonymisierung ist entscheidend um Risiken zu adressieren, aber schwierig weil gleichzeitig die Verhaltensaspekte des Event Logs erhalten werden sollen. Dies führt zu einem Privacy-Utility-Trade-Off. Dieser wird durch neue Algorithmen wie SaCoFa und SaPa angegangen, die Differential Privacy garantieren und gleichzeitig Utility erhalten. PRIPEL ergänzt die anonymiserten Control-flows um Kontextinformationen und ermöglich so die Veröffentlichung von vollständigen, geschützten Logs. Mit PRETSA wird eine Algorithmenfamilie vorgestellt, die k-anonymity garantiert. Dafür werden privacy-verletztende Traces miteinander vereint, mit dem Ziel ein möglichst syntaktisch ähnliches Log zu erzeugen. Durch Experimente kann eine bessere Utility-Erhaltung gegenüber existierenden Lösungen aufgezeigt werden.Process mining analyzes business processes using event logs. Each activity execution is recorded as an event in a trace, representing a process instance's behavior. Traces often hold sensitive info like patient data. This thesis addresses privacy concerns arising from trace data and process mining. A re-identification risk study on public event logs reveals high risk, but other threats exist. Anonymization is vital to address these issues, yet challenging due to preserving behavioral aspects for analysis, leading to a privacy-utility trade-off. New algorithms, SaCoFa and SaPa, are introduced for trace anonymization using noise for differential privacy while maintaining utility. PRIPEL supplements anonymized control flows with trace contextual info for complete protected logs. For k-anonymity, the PRETSA algorithm family merges privacy-violating traces based on a prefix representation of the event log, maintaining syntactic similarity. Empirical evaluations demonstrate utility improvements over existing techniques

Privaatsuskaitse tehnoloogiaid äriprotsesside kaeveks

Protsessikaeve tehnikad võimaldavad organisatsioonidel analüüsida protsesside täitmise käigus tekkivaid logijälgi eesmärgiga leida parendusvõimalusi. Nende tehnikate eelduseks on, et nimetatud logijälgi koondavad sündmuslogid on andmeanalüütikutele analüüside läbi viimiseks kättesaadavad. Sellised sündmuslogid võivad sisaldada privaatset informatsiooni isikute kohta kelle jaoks protsessi täidetakse. Sellistel juhtudel peavad organisatsioonid rakendama privaatsuskaitse tehnoloogiaid (PET), et võimaldada analüütikul sündmuslogi põhjal järeldusi teha, samas säilitades isikute privaatsust. Kuigi PET tehnikad säilitavad isikute privaatsust organisatsiooni siseselt, muudavad nad ühtlasi sündmuslogisid sellisel viisil, mis võib viia analüüsi käigus valede järeldusteni. PET tehnikad võivad lisada sündmuslogidesse sellist uut käitumist, mille esinemine ei ole reaalses sündmuslogis võimalik. Näiteks võivad mõned PET tehnikad haigla sündmuslogi anonüümimisel lisada logijälje, mille kohaselt patsient külastas arsti enne haiglasse saabumist. Käesolev lõputöö esitab privaatsust säilitavate lähenemiste komplekti nimetusega privaatsust säilitav protsessikaeve (PPPM). PPPM põhiline eesmärk on leida tasakaal võimaliku sündmuslogi analüüsist saadava kasu ja analüüsile kohaldatavate privaatsusega seonduvate regulatsioonide (näiteks GDPR) vahel. Lisaks pakub käesolev lõputöö lahenduse, mis võimaldab erinevatel organisatsioonidel protsessikaevet üle ühise andmete terviku rakendada, ilma oma privaatseid andmeid üksteisega jagamata. Käesolevas lõputöös esitatud tehnikad on avatud lähtekoodiga tööriistadena kättesaadavad. Nendest tööriistadest esimene on Amun, mis võimaldab sündmuslogi omanikul sündmuslogi anonüümida enne selle analüütikule jagamist. Teine tööriist on Libra, mis pakub täiendatud võimalusi kasutatavuse ja privaatsuse tasakaalu leidmiseks. Kolmas tööriist on Shareprom, mis võimaldab organisatsioonidele ühiste protsessikaartide loomist sellisel viisil, et ükski osapool ei näe teiste osapoolte andmeid.Process Mining Techniques enable organizations to analyze process execution traces to identify improvement opportunities. Such techniques need the event logs (which record process execution) to be available for data analysts to perform the analysis. These logs contain private information about the individuals for whom a process is being executed. In such cases, organizations need to deploy Privacy-Enhancing Technologies (PETs) to enable the analyst to drive conclusions from the event logs while preserving the privacy of individuals. While PETs techniques preserve the privacy of individuals inside the organization, they work by perturbing the event logs in such a way that may lead to misleading conclusions of the analysis. They may inject new behaviors into the event logs that are impossible to exist in real-life event logs. For example, some PETs techniques anonymize a hospital event log by injecting a trace that a patient may visit a doctor before checking in inside the hospital. In this thesis, we propose a set of privacy-preserving approaches that we call Privacy-Preserving Process Mining (PPPM) approaches to strike a balance between the benefits an analyst can get from analyzing these event logs and the requirements imposed on them by privacy regulations (e.g., GDPR). Also, in this thesis, we propose an approach that enables organizations to jointly perform process mining over their data without sharing their private information. The techniques proposed in this thesis have been proposed as open-source tools. The first tool is Amun, enabling an event log publisher to anonymize their event log before sharing it with an analyst. The second tool is called Libra, which provides an enhanced utility-privacy tradeoff. The third tool is Shareprom, which enables organizations to construct process maps jointly in such a manner that no party learns the data of the other parties.https://www.ester.ee/record=b552434