Gianluca Anselmi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Gianluca Anselmi has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Anna Maria Mandalari
Department of Electronic and Electrical Engineering, University College London, London WC1E 7JE, UK
3 shared publications - Sara Lazzaro
DIIES Department, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy
2 shared publications - Vincenzo De Angelis
DIMES, University of Calabria, Rende, CS, Italy
2 shared publications - Alexander D'SOuza
University of California, Davis, Davis, USA
1 shared publication - Patricia Callejo
Telematic Engineering Department, Universidad Carlos III of Madrid, Legans, Spain
1 shared publication - Yash Vekaria
University of California, Davis, Davis, USA
1 shared publication
Latest Publications
- Open Access
- 4 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 622-634
Watching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs
Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matching it against a content library to detect what content is being displayed at any given point in time. While prior research has investigated third-party tracking in the smart TV ecosystem, it has not looked into second-party ACR tracking that is directly conducted by the smart TV platform. In this work, we conduct a black-box audit of ACR network traffic between ACR clients on the smart TV and ACR servers. We use our auditing approach to systematically investigate whether (1) ACR tracking is agnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2) privacy controls offered by smart TVs have an impact on ACR tracking, and (3) there are any differences in ACR tracking between the UK and the US. We perform a series of experiments on two major smart TV platforms: Samsung and LG. Our results show that ACR works even when the smart TV is used as a ''dumb'' external display, opting-out stops network traffic to ACR servers, and there are differences in how ACR works across the UK and the US.
- 4 November 2024
- Published by Association for Computing Machinery (ACM)
- p. 622-634
Open AccessWatching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs
Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matching it against a content library to detect what content is being displayed at any given point in time. While prior research has investigated third-party tracking in the smart TV ecosystem, it has not looked into second-party ACR tracking that is directly conducted by the smart TV platform. In this work, we conduct a black-box audit of ACR network traffic between ACR clients on the smart TV and ACR servers. We use our auditing approach to systematically investigate whether (1) ACR tracking is agnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2) privacy controls offered by smart TVs have an impact on ACR tracking, and (3) there are any differences in ACR tracking between the UK and the US. We perform a series of experiments on two major smart TV platforms: Samsung and LG. Our results show that ACR works even when the smart TV is used as a ''dumb'' external display, opting-out stops network traffic to ACR servers, and there are differences in how ACR works across the UK and the US.
- Open Access
- 2 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-3
COPSEC: Compliance-Oriented IoT Security and Privacy Evaluation Framework
A rising number of Internet of Things (IoT) security and privacy threats have been documented over the last few years. However, IoT devices' domain designs are out-of-date and do not take into consideration the changing dangers associated with them. In this paper, we present COPSEC, a novel framework for evaluating whether IoT devices are compliant with security guidelines and privacy regulations. We extract metrics from existing guidelines and regulations and test them on a set of devices by performing hundreds of automated experiments. Our results indicate not only that these devices are not compliant with basic security guidelines, but also that their data collection operations may introduce privacy risks for the users that adopt them.
- 2 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-3
Open AccessCOPSEC: Compliance-Oriented IoT Security and Privacy Evaluation Framework
A rising number of Internet of Things (IoT) security and privacy threats have been documented over the last few years. However, IoT devices' domain designs are out-of-date and do not take into consideration the changing dangers associated with them. In this paper, we present COPSEC, a novel framework for evaluating whether IoT devices are compliant with security guidelines and privacy regulations. We extract metrics from existing guidelines and regulations and test them on a set of devices by performing hundreds of automated experiments. Our results indicate not only that these devices are not compliant with basic security guidelines, but also that their data collection operations may introduce privacy risks for the users that adopt them.