TY - GEN
T1 - Privacy-Preserving Incentive Systems with Highly Efficient Point-Collection
AU - Bobolz, Jan
AU - Eidens, Fabian
AU - Krenn, Stephan
AU - Slamanig, Daniel
AU - Striecks, Christoph
N1 - Funding Information:
This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Centre On-The-Fly Computing (GZ: SFB 901/3) under the project number 160364472 and by the European Union under the H2020 Programme Grant Agreement No. 830929 (CyberSec4Europe).
Publisher Copyright:
© 2020 ACM.
PY - 2020/10/5
Y1 - 2020/10/5
N2 - Incentive systems (such as customer loyalty systems) are omnipresent nowadays and deployed in several areas such as retail, travel, and financial services. Despite the benefits for customers and companies, this involves large amounts of sensitive data being transferred and analyzed. These concerns initiated research on privacy-preserving incentive systems, where users register with a provider and are then able to privately earn and spend incentive points. In this paper we construct an incentive system that improves upon the state-of-the-art in several ways: (1) We improve efficiency of the Earn protocol by replacing costly zero-knowledge proofs with a short structure-preserving signature on equivalence classes. (2) We enable tracing of remainder tokens from double-spending transactions without losing backward unlinkability. (3) We allow for secure recovery of failed Spend protocol runs (where usually, any retries would be counted as double-spending attempts). (4) We guarantee that corrupt users cannot falsely blame other corrupt users for their double-spending. We propose an extended formal model of incentive systems and a concrete instantiation using homomorphic Pedersen commitments, ElGamal encryption, structure-preserving signatures on equivalence classes (SPS-EQ), and zero-knowledge proofs of knowledge. We formally prove our construction secure and present benchmarks showing its practical efficiency.
AB - Incentive systems (such as customer loyalty systems) are omnipresent nowadays and deployed in several areas such as retail, travel, and financial services. Despite the benefits for customers and companies, this involves large amounts of sensitive data being transferred and analyzed. These concerns initiated research on privacy-preserving incentive systems, where users register with a provider and are then able to privately earn and spend incentive points. In this paper we construct an incentive system that improves upon the state-of-the-art in several ways: (1) We improve efficiency of the Earn protocol by replacing costly zero-knowledge proofs with a short structure-preserving signature on equivalence classes. (2) We enable tracing of remainder tokens from double-spending transactions without losing backward unlinkability. (3) We allow for secure recovery of failed Spend protocol runs (where usually, any retries would be counted as double-spending attempts). (4) We guarantee that corrupt users cannot falsely blame other corrupt users for their double-spending. We propose an extended formal model of incentive systems and a concrete instantiation using homomorphic Pedersen commitments, ElGamal encryption, structure-preserving signatures on equivalence classes (SPS-EQ), and zero-knowledge proofs of knowledge. We formally prove our construction secure and present benchmarks showing its practical efficiency.
KW - incentive systems
KW - privacy
KW - provable security
UR - http://www.scopus.com/inward/record.url?scp=85095165119&partnerID=8YFLogxK
U2 - 10.1145/3320269.3384769
DO - 10.1145/3320269.3384769
M3 - Conference contribution
AN - SCOPUS:85095165119
T3 - Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020
SP - 319
EP - 333
BT - Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020
PB - Association for Computing Machinery, Inc
T2 - 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020
Y2 - 5 October 2020 through 9 October 2020
ER -