Privacy Enhanced Secure Tropos: Modelleerimiskeel Euroopa Liidu isikuandmete kaitse üldmääruse (GDPR) vastavuse jaoks
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2018
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Euroopa Liidu isikuandmete kaitse üldmäärusele (GDPR) vastavuse tagamine saab õiguslikult hädavajalikuks kõigis tarkvarasüsteemides, mis töötlevad ja haldavad isikuandmeid. Sellest tulenevalt tuleb GDPR vastavuse ja privaatsuse komponentidega arvestada arendusprotsessi varajastes etappides ning tarkvarainsenerid peaksid analüüsima mitte ainult süsteemi, vaid ka selle keskkonda. Käesolev uuring keskendub viimasel ajal tähepepanu pälvinud modelleerimiskeelele Privacy Enhanced Secure Tropos (PESTOS), mis põhineb Tropos metoodikal, hõlmates eesmärkide ja reeglite vaatenurka, mis aitab tarkvarainseneridel hinnata erinevaid Privacy-enhancing Technologies (PET-e) kandidaate, arendades samas privaatsustundlikke süsteeme, et need oleksid GDPR-iga kooskõlas.Kuigi GDPR artikli 5 lõikes 2 sätestatakse, et vastutuse põhimõtte kohaselt peavad organisatsioonid suutma näidata vastavust GDPR põhimõtetele (meie teadmiste kohaselt ei ole praegu veel ühtegi teist privaatsuse modelleerimise keelt, mis keskendub eelkõige GDPR nõuetele ja mis põhineb Security Risk-Aware Secure Tropos metoodikal), ei olnud saadaval ühtegi praktilist modelleerimise keelt, mis rahuldaks tööstus- ja ärivajadusi. See on Euroopa Liidu piirkonna avalikele asutustele ja erasektorile tõsine probleem, kuna GDPR toob vastutavatele ja volitatud töötlejatele kaasa väga tõsiseid trahve. Organisatsioonid ei oma piisavat kindlustunnet regulatsioonide täitmise osas ja tarkvarainseneridel puuduvad meetodid saamaks ülevaadet infosüsteemide muutmistaotlustest. Käesolevas lõputöös rakendatakse struktureeritud privaatsuse modelleerimise keelt, mida nimetatakse PESTOS-iks. Selle eesmärk on tagada kõrgetasemeline vastavus GDPR nõuetele kattes PET-e eesmärk-tegija-reegel perspektiivis hindamiseks ka lõimitud andmekaitse põhimõtted. GDPR 99-st artiklist 21 artiklit saab identifitseerida tehniliste nõudmistena, mile osas PESTOS suudab ettvõtetel aidata GDPR-ist tulenevaid kohustusi täita. Identiteedi- ja turvaekspertide seas läbiviidud uuring kinnitab, et kavandatud mudelil on piisav õigsus, täielikkus, tootlikkus ja kasutusmugavus.
The European Union General Data Protection Regulation (GDPR) compliance is becoming a legal necessity for software systems that process and manage personal data. As a result of that fact, GDPR compliance and privacy components need to be considered from the early stages of the development process and software engineers should analyze not only the system but also its environment. Hereby with this study, Privacy Enhanced Secure Tropos (PESTOS) is emerging as a privacy modeling language based on Tropos methodology, which covers the goal and rule perspective, for helping software engineers by assessing candidate PETs, while designing privacy-aware systems, in order to make them compatible with GDPR. Although in Article 5(2) of the GDPR, the accountability principle requires organizations to show compliance with the principles of the GDPR, (To the best of our knowledge, currently there is no other privacy modeling language especially focuses on the GDPR compliance and enhanced based on Security Risk-Aware Secure Tropos methodology) there were not any practical social modeling languages supply the demand driven by industrial and commercial needs. This is a serious issue for public institutions and private sector in EU-zone because GDPR brings very serious charges for data controllers and data processors, therefore organizations do not feel themselves ready to face with those regulations and software engineers have a lack of methods for capturing change requests of the information systems. This paper applies a structured privacy modeling language that is called as PESTOS which has a goal-oriented solution domain that aims to bring a high compatibility with GDPR by covering Privacy by Design strategies for assessing proper privacy-enhancing technologies(PETs) in a respect of the goal-actor-rule perspective. Among the 99 articles of GDPR, 21 articles can be identified as technical level of requirements that PESTOS is able to transform them into GDPR goals needs to be fulfilled in order to support business assets. A survey conducted by identity and security experts validates that proposed model has a sufficient level of correctness, completeness, productivity and ease of use.
The European Union General Data Protection Regulation (GDPR) compliance is becoming a legal necessity for software systems that process and manage personal data. As a result of that fact, GDPR compliance and privacy components need to be considered from the early stages of the development process and software engineers should analyze not only the system but also its environment. Hereby with this study, Privacy Enhanced Secure Tropos (PESTOS) is emerging as a privacy modeling language based on Tropos methodology, which covers the goal and rule perspective, for helping software engineers by assessing candidate PETs, while designing privacy-aware systems, in order to make them compatible with GDPR. Although in Article 5(2) of the GDPR, the accountability principle requires organizations to show compliance with the principles of the GDPR, (To the best of our knowledge, currently there is no other privacy modeling language especially focuses on the GDPR compliance and enhanced based on Security Risk-Aware Secure Tropos methodology) there were not any practical social modeling languages supply the demand driven by industrial and commercial needs. This is a serious issue for public institutions and private sector in EU-zone because GDPR brings very serious charges for data controllers and data processors, therefore organizations do not feel themselves ready to face with those regulations and software engineers have a lack of methods for capturing change requests of the information systems. This paper applies a structured privacy modeling language that is called as PESTOS which has a goal-oriented solution domain that aims to bring a high compatibility with GDPR by covering Privacy by Design strategies for assessing proper privacy-enhancing technologies(PETs) in a respect of the goal-actor-rule perspective. Among the 99 articles of GDPR, 21 articles can be identified as technical level of requirements that PESTOS is able to transform them into GDPR goals needs to be fulfilled in order to support business assets. A survey conducted by identity and security experts validates that proposed model has a sufficient level of correctness, completeness, productivity and ease of use.