TY - GEN
T1 - Prov-Trust
T2 - 17th International Conference on Security and Cryptography, SECRYPT 2020 - Part of the 17th International Joint Conference on e-Business and Telecommunications, ICETE 2020
AU - Kaaniche, Nesrine
AU - Belguith, Sana
AU - Laurent, Maryline
AU - Gehani, Ashish
AU - Russello, Giovanni
N1 - Publisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Data provenance refers to records of the inputs, entities, systems, and processes that influence data of interest, providing a historical record of the data and its origins. Secure data provenance is vital to ensure accountability, forensics investigation of security attacks and privacy preservation. In this paper, we propose Prov-Trust, a decentralized and auditable SGX-based data provenance system relying on highly distributed ledgers. This consensually shared and synchronized database allows anchored data to have public witness, providing tamper-proof provenance data, enabling the transparency of data accountability, and enhancing the secrecy and availability of the provenance data. Prov-Trust relies on Intel SGX enclave to ensure a trusted execution of the provenance kernel to collect, store and query provenance records. The use of SGX enclave protects data provenance and users' credentials against malicious hosting and processing parties. Prov-Trust does not rely on a trusted third party to store provenance data while performing their verification using smart contracts and voting process. The storage of the provenance data in Prov-Trust is done using either the log events of Smart Contracts or blockchain's transactions depending on the provenance change event, which enables low storage costs. Finally, Prov-Trust ensures an accurate privacy-preserving auditing process based on blockchain traces and achieved thanks to events' logs that are signed by SGX enclaves, transactions being registered after each vote session, and sealing the linking information using encryption schemes.
AB - Data provenance refers to records of the inputs, entities, systems, and processes that influence data of interest, providing a historical record of the data and its origins. Secure data provenance is vital to ensure accountability, forensics investigation of security attacks and privacy preservation. In this paper, we propose Prov-Trust, a decentralized and auditable SGX-based data provenance system relying on highly distributed ledgers. This consensually shared and synchronized database allows anchored data to have public witness, providing tamper-proof provenance data, enabling the transparency of data accountability, and enhancing the secrecy and availability of the provenance data. Prov-Trust relies on Intel SGX enclave to ensure a trusted execution of the provenance kernel to collect, store and query provenance records. The use of SGX enclave protects data provenance and users' credentials against malicious hosting and processing parties. Prov-Trust does not rely on a trusted third party to store provenance data while performing their verification using smart contracts and voting process. The storage of the provenance data in Prov-Trust is done using either the log events of Smart Contracts or blockchain's transactions depending on the provenance change event, which enables low storage costs. Finally, Prov-Trust ensures an accurate privacy-preserving auditing process based on blockchain traces and achieved thanks to events' logs that are signed by SGX enclaves, transactions being registered after each vote session, and sealing the linking information using encryption schemes.
KW - Blockchain
KW - Data integrity
KW - Data provenance
KW - Intel sGX
KW - Privacy preserving
UR - https://www.scopus.com/pages/publications/85111138820
U2 - 10.5220/0009889302250237
DO - 10.5220/0009889302250237
M3 - Conference contribution
AN - SCOPUS:85111138820
T3 - ICETE 2020 - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications
SP - 225
EP - 237
BT - ICETE 2020 - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications
A2 - Callegari, Christian
A2 - Ng, Soon Xin
A2 - Sarigiannidis, Panagiotis
A2 - Battiato, Sebastiano
A2 - de Leon, Angel Serrano Sanchez
A2 - Ksentini, Adlen
A2 - Lorenz, Pascal
A2 - Obaidat, Mohammad
A2 - Obaidat, Mohammad
A2 - Obaidat, Mohammad
PB - SciTePress
Y2 - 8 July 2020 through 10 July 2020
ER -