Privacy risk assessment and privacy-preserving data monitoring

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302GN7" target="_blank" >RIV/61988987:17310/22:A2302GN7 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417422003153" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417422003153</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2022.116867" target="_blank" >10.1016/j.eswa.2022.116867</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Privacy risk assessment and privacy-preserving data monitoring

Popis výsledku v původním jazyce

Privacy regulations press organisations to handle personal data with reinforced caution. Moreover, organisations are dealing with increasing amounts of Personally Identifiable Information in their systems. Thus, there is a high demand not only for privacy-preserving data processing mechanisms but also privacy-enhancing services. As such, we propose the Personal Data Analyser, a tool that increases privacy assurances and minimises privacy risks through automated privacy-preserving data monitoring and privacy risk assessment mechanisms. Automated data monitoring is achieved with a hybrid mechanism that employs Regular Expressions, Natural Language Processing tools, and machine learning models such as Multilayer Perceptron and Random Forests. Our privacy risk assessment mechanism is based on custom-built crisp and fuzzy models, that consider information such as data processor reputation, data sensitiveness and other inputs in order to assess privacy risk associated with data transactions. Our work is integrated and validated under real use cases of the PoSeID-on platform and warns users whenever potential privacy risks are detected. Validation under PoSeID-on’s pilots and its users proved beneficial not only to assess our solution but also to raise users’ awareness of their data. The results of this work show that our solution is an effective Privacy Enhancing Technology that increases privacy assurances between organisations and their users.

Název v anglickém jazyce

Privacy risk assessment and privacy-preserving data monitoring

Popis výsledku anglicky

Privacy regulations press organisations to handle personal data with reinforced caution. Moreover, organisations are dealing with increasing amounts of Personally Identifiable Information in their systems. Thus, there is a high demand not only for privacy-preserving data processing mechanisms but also privacy-enhancing services. As such, we propose the Personal Data Analyser, a tool that increases privacy assurances and minimises privacy risks through automated privacy-preserving data monitoring and privacy risk assessment mechanisms. Automated data monitoring is achieved with a hybrid mechanism that employs Regular Expressions, Natural Language Processing tools, and machine learning models such as Multilayer Perceptron and Random Forests. Our privacy risk assessment mechanism is based on custom-built crisp and fuzzy models, that consider information such as data processor reputation, data sensitiveness and other inputs in order to assess privacy risk associated with data transactions. Our work is integrated and validated under real use cases of the PoSeID-on platform and warns users whenever potential privacy risks are detected. Validation under PoSeID-on’s pilots and its users proved beneficial not only to assess our solution but also to raise users’ awareness of their data. The results of this work show that our solution is an effective Privacy Enhancing Technology that increases privacy assurances between organisations and their users.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

—

Číslo periodika v rámci svazku

15 August 2022

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

000794359900007

EID výsledku v databázi Scopus

2-s2.0-85127530748