| Title | Applying Privacy-Aware Policies in IoT Devices Using Privacy Metrics |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Tavakolan, Mona, Faridi, Ismaeel A. |
| Conference Name | 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) |
| Keywords | clustering, Computer hacking, data privacy, Human Behavior, Indexes, Internet of Things, loT, Measurement, privacy, Privacy Policies, privacy-aware policies, pubcrawl, Scalability, security |
| Abstract | In recent years, user's privacy has become an important aspect in the development of Internet of Things (IoT) devices. However, there has been comparatively little research so far that aims to understanding user's privacy in connection with IoT. Many users are worried about protecting their personal information, which may be gathered by IoT devices. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value. |
| DOI | 10.1109/CCCI49893.2020.9256605 |
| Citation Key | tavakolan_applying_2020 |
Applying Privacy-Aware Policies in IoT Devices Using Privacy Metrics