Skip to main content

Researching the Darkside of Internet Filtering

The OpenNet Initiative (ONI) is a collaborative partnership of four leading academic institutions -- the Citizen Lab at the Munk Centre for International Studies, University of Toronto; the Berkman Center for Internet & Society at Harvard Law School; the Advanced Network Research Group at the Cambridge Security Programme, University of Cambridge; and the Oxford Internet Institute, Oxford University.

Their aim is to investigate, expose and analyze Internet filtering and surveillance practices in a credible and non-partisan fashion. They intend to uncover the potential pitfalls and unintended consequences of these practices, and thus help to inform better public policy and advocacy work in this area. To achieve these aims, the ONI employs a unique multi-disciplinary approach that includes:

- Development and deployment of a suite of technical enumeration tools and core methodologies for the study of Internet filtering and survellance;

- Capacity-building among networks of local advocates and researchers;

- Advanced studies exploring the consequences of current and future trends and trajectories in filtering and surveillance practices, and their implications for domestic and international law and governance regimes.

Popular posts from this blog

AI Agents Have Memory. Where is The Security?

Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...