T-Tracer
Liang Wang, Jun Li, Lina Zuo, Yu Jie Wen, Haibo Liu, Wenyuan Liu · 2022
In a data sharing network where parties lack mutual trust, credible traitor tracing is indispensable to reliable data delivery. Currently, the main obstacles for traitor tracing are the deficiency of content-independent data watermarking scheme and the untrustworthiness of third parties. In view of this, we propose the T-Tracer --- an encoding-based watermarking scheme. With T-Tracer, each party of data delivery embeds its digital fingerprint into the byte sequence of the shared data by generating a tailor-made symbol mapping table, whereby the watermarking can handle any type of data. A blockchain network overlaying all data sharing participants serves as a trusted third party to ensure the credibility of T-Tracer. By applying T-Tracer, anyone in the network can recognize the culprits by identifying their fingerprints from the data objects that appear as evidence of data delivery repudiation. The evaluation results show that T-Tracer is effective and practical in improving the credibility of traitor tracing.