Grassroots organizing requires establishing trust in digital artifacts (like event announcements or calls to action) while navigating significant security threats including surveillance, infiltration, and state violence. Traditional trust infrastructures like PKI and Web of Trust fail to address these specific needs, as they create public records of trust relationships that can expose activist networks and require institutional involvement that may be inaccessible or dangerous for marginalized communities. To address this, we introduce tigro, a novel trust infrastructure and system designed specifically for grassroots organizing contexts. Unlike conventional trust infrastructures, tigro implements a two-tier trust model: ground trust, which cryptographically binds digital annotations to physically vetted individuals, and artifact trust, which enables private, need-to-know sharing of assessments about digital content via annotations. Our protocol begins with an in-person key exchange that establishes a shared cryptographic key, creating a secure bridge between activists' existing physical vetting practices and their digital trust needs. To realize this approach, we define a new cryptographic primitive called an encrypted annotation system (EAS) and construct tigro using structured encryption and anonymous channels. We present two implementations with different security-performance tradeoffs: an efficient version for practical deployment that handles annotations in under a second, and a subliminal version that reveals virtually no metadata. Through this design, tigro enables activists to securely verify digital content without compromising relationship privacy or creating surveillance vulnerabilities, addressing a critical gap in existing trust infrastructure.

The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.