Building a Privacy-Preserving Smart Camera System

Abstract

Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such as service providers (e.g., the cloud). Third parties therefore can (and in some instances have) access the video footage without the users’ knowledge or consent—violating the core tenet of user privacy. In this paper, we present CaCTUs, a privacy-preserving smart Camera system Controlled Totally by Users. CaCTUs returns control to the user; the root of trust begins with the user and is maintained through a series of cryptographic protocols, designed to support popular features, such as sharing, deleting, and viewing videos live. We show that the system can support live streaming with a latency of 2 s at a frame rate of 10 fps and a resolution of 480 p. In so doing, we demonstrate that it is feasible to implement a performant smart-camera system that leverages the convenience of a cloud-based model while retaining the ability to control access to (private) data.

Protecting Privacy in Surveillance Systems via Selective Video Encryption

“…Advantages are can be used in many type of data, time efficient, adaptable. Limitations are problem in adaptability, energy intensive, Yohan Beugin [8] et.al discussed "Building a secure and privacy-preserving smart camera system", The problem statement discussed is that current commercially available smart camera systems often prioritize convenience and ease of use over security and privacy, leaving users vulnerable to potential privacy violations. This work aims to address this issue by proposing a usercontrolled smart camera system that prioritizes privacy and security while maintaining usability and functionality.…”

Interest-disclosing Mechanisms for Advertising are Privacy-Exposing (not Preserving)

“…Our approach has several advantages: (1) it does not require the collection or use of any sensitive data by researchers, (2) the generated histories have the same desired properties of representativeness as the global distributions see on the web by Cloudflare, Google, and Mozilla because they are directly drawn from their reported results, and (3) it allows the generation of any arbitrarily large synthetic dataset than can (4) be publicly shared to (5) enable reproducible methodologies. As such, we release as an open-source artifact 5 the entirety of our generation code and of our privacy and utility analyses of the Topics API performed for this paper [5].…”

“…For each domain, we end up mapping these categories to a set of topics that we can compare to the ones predicted by Topics. We release our manual mapping as part of our open-source artifact [5].…”

“…This appears to directly violate the privacy goals proposed by Google with Topics over TPCs. On the utility perspective, we see that (5) Topics is quite useful to advertisers. On average, the Topics API returns at least 1 true topic aligned with user interests in about 60% of cases-assuming the API is used faithfully.…”