@article{yoon_2023, title={AccountNet: Accountable Data Propagation Using Verifiable Peer Shuffling}, ISSN={["1063-6927"]}, DOI={10.1109/ICDCS57875.2023.00050}, abstractNote={Collecting evidence of data that software systems produce and consume can provide critical information for reconstructing erratic behavior, tracing the origin of faults, and thus holding the responsible party accountable for particular consequences. However, when data propagates across trust boundaries with conflicting interests, they can be tempted to make evidence unprovable in order to avoid potential liability. Hence, we present a data propagation protocol that makes such attempts either detectable or ineffective by having data transfers witnessed by other network participants that collectively act as the prover for the data propagation process. The protocol builds an unstructured peer-to-peer overlay and is designed to disincentivize collusion among a malicious coalition of nodes by enforcing network participants to constantly exchange their partial views on the network in a random yet verifiable manner. A data producer or consumer who does not faithfully follow the protocol ends up having fewer witnesses from their side, making the network resistant to collusion with high probability. We derive the conditions under which this property holds and demonstrate the practicality and cost of our approach through the implementation of a distributed application built on top of the proposed protocol.}, journal={2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS}, author={Yoon, Man-Ki}, year={2023}, pages={48–61} }