Zooming Into Video Conferencing Privacy
Bibliographic Data
| ID | 22107246 |
|---|---|
| Authors | Dima Kagan (0000-0002-8216-8776, Ben-Gurion University of the Negev), Galit Fuhrmann Alpert (0000-0003-4778-3754, Ben-Gurion University of the Negev), Michael Fire (0000-0002-6075-2568, Ben-Gurion University of the Negev) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Pages | 933-944 |
| Publication date | 2024-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3231987 |
| OpenAlex | W4313423374 |
| Language | EN |
| Citations received | 2 |
| References cited | 29 |
The unprecedented growth in video conferencing usage is accompanied by multiple security and privacy threats. Importantly, protecting users’ privacy is not always in their own hands. Posting meeting images affects all participants, leading to an easy collection of personal data including age, gender and linkage with participation in other meetings. Here, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted online. We used image processing, text recognition tools, as well as social network analysis to explore our curated dataset of over 15 700 collage images, and over 142 000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and even full names. This type of data can vastly and easily jeopardize people’s security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual
Image sharing · Internet privacy · Multimedia · Social media · Sociology · Videoconferencing · World Wide Web · Zoom · Computer Science · Cybercrime and Law Enforcement Studies · Face recognition and analysis · Spam and Phishing Detection · Artificial Intelligence
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |