The TV is Smart and Full of Trackers: Towards Understanding the Smart TV Advertising and Tracking Ecosystem

TL;DR

This study provides a comprehensive analysis of smart TV network traffic, revealing widespread advertising and tracking activities, platform-specific ecosystems, and the limited effectiveness of existing DNS-based blocklists in protecting user privacy.

Contribution

It offers the first large-scale measurement of smart TV traffic, compares different platforms, and evaluates the effectiveness of ad-blocking tools tailored for smart TVs.

Findings

Smart TVs frequently connect to advertising and tracking services.

Different platforms have distinct ATS ecosystems.

DNS-based blocklists are partially effective in blocking tracking.

Abstract

Motivated by the growing popularity of smart TVs, we present a large-scale measurement study of smart TVs by collecting and analyzing their network traffic from two different vantage points. First, we analyze aggregate network traffic of smart TVs in-the-wild, collected from residential gateways of tens of homes and several different smart TV platforms, including Apple, Samsung, Roku, and Chromecast. In addition to accessing video streaming and cloud services, we find that smart TVs frequently connect to well-known as well as platform-specific advertising and tracking services (ATS). Second, we instrument Roku and Amazon Fire TV, two popular smart TV platforms, by setting up a controlled testbed to systematically exercise the top-1000 apps on each platform, and analyze their network traffic at the granularity of the individual apps. We again find that smart TV apps connect to a wide range of ATS, and that the key players of the ATS ecosystems of the two platforms are different from each other and from that of the mobile platform. Third, we evaluate the (in)effectiveness of state-of-the-art DNS-based blocklists in filtering advertising and tracking traffic for smart TVs. We find that personally identifiable information (PII) is exfiltrated to platform-related Internet endpoints and third parties, and that blocklists are generally better at preventing exposure of PII to third parties than to platform-related endpoints. Our work demonstrates the segmentation of the smart TV ATS ecosystem across platforms and its differences from the mobile ATS ecosystem, thus motivating the need for designing privacy-enhancing tools specifically for each smart TV platform.