Privacy-Preserving Solutions in Hybrid Sensing, Anonymous Crowdsourcing and Verifiable Algorithmic Decision-Making

Abstract

This thesis advances privacy-preserving solutions essential for addressing contemporary technological challenges in smart cities, decentralized systems, and algorithmic decision-making processes. Firstly, we introduce a hybrid sensing framework integrating Internet of Things (IoT) sensors and crowdsensing techniques to overcome limitations inherent in traditional methods. The hybrid sensing model incentivizes voluntary user contributions to complement fixed-location IoT sensors, ensuring reliable and comprehensive data collection while maintaining user anonymity through a privacy-preserving protocol. We implement this model in a smart parking application, demonstrating significant improvements in data accuracy and user engagement.

Secondly, we propose a decentralized anonymous crowdsourcing system leveraging blockchain technology, which removes reliance on centralized intermediaries, thereby enhancing transparency and mitigating biases. Our system integrates anonymous payments using the Zerocoin protocol framework, eliminating the need for worker identity registration and trusted setups, thus fostering genuinely anonymous participation. Empirical analyses confirm that our approach maintains practical efficiency in transaction verification and moderate blockchain gas costs.

Lastly, we tackle fairness and transparency in algorithmic decision-making processes, addressing public concerns regarding inherent biases and opaque computational practices. We develop a privacy-preserving, publicly verifiable framework that combines succinct zero-knowledge proofs with blockchain infrastructure, allowing independent verification of algorithmic fairness without exposing sensitive inputs or decision-making algorithms. Our concrete instantiation employs a restricted KZG polynomial commitment scheme alongside the Sonic zk-SNARK protocol, demonstrating small proof sizes, efficient verification, and practical deployment feasibility.

Collectively, this thesis contributes significantly to the field by providing robust, scalable, and privacy-conscious technologies tailored for contemporary smart city applications and decentralized computational ecosystems. Show more