  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.
