Winter 2026 Week 8

Fri, 27 Feb 2026 00:00:00 +0000

Title	Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
Authors	Ted Shaowang, Shinan Liu, Jonatas Marques, Nick Feamster, Sanjay Krishnan
Abstract	Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. This raises significant privacy concerns, necessitating the application of data minimization – a foundational principle in emerging data regulations, which mandates that service providers only collect data that is directly relevant and necessary for a specified purpose. Despite its importance, data minimization lacks a precise technical definition in the context of sensor data, where collections of weak signals make it challenging to apply a binary “relevant and necessary” rule. This paper provides a technical interpretation of data minimization in the context of sensor streams, explores practical methods for implementation, and addresses the challenges involved. Through our approach, we demonstrate that our framework can reduce user identifiability by up to 16.7% while maintaining accuracy loss below 1%, offering a viable path toward privacy-preserving IoT data processing.

PaPrica-PS: Fine-Grained, Dynamic Access Control Policy Enforcement for Pub/Sub Systems

Wed, 26 Nov 2025 00:00:00 +0000

High-volume publish/subscribe (pub/sub) systems include collections of hardware and software components such as IoT sensors and the protocols that connect them. Many of these have heretofore lacked robust security and privacy controls by default despite there being significant security, safety, and privacy implications driving the need to control access to the data they generate and manage.

Examples of such pub/sub-based systems are those which power critical systems from smart buildings and factories to full city-wide device networks. In this project, we are developing a fine-grained access control model and enforcement mechanism to address this gap. Our proposed FGAC model builds upon Attribute-Based Access Control (ABAC) defining access rules based on the MQTT protocol message “topics”, attributes of the subscribers and publishers to those topics, as well as ephemeral and per-message context information.

Our framework is platform-agnostic and we implement the prototype for our experiments based on an off-the-shelf open source MQTT pub/sub system without altering the base code of that server itself.

Jacob Olinger | DIPr Lab at PSU

Winter 2026 Week 8

PaPrica-PS: Fine-Grained, Dynamic Access Control Policy Enforcement for Pub/Sub Systems