1.
Oscar G. Bautista, Mohammad Hossein Manshaei, Richard Hernandez, Kemal Akkaya, Soamar Homsi, Selcuk Uluagac
MPC-ABC: Blockchain-Based Network Communication for Efficiently Secure Multiparty Computation Journal Article
Springer Journal of Network and Systems Management Journal, 2023.
Abstract | Links | BibTeX | Tags: Secure Multipart Computation
@article{OscarSecure2023,
title = {MPC-ABC: Blockchain-Based Network Communication for Efficiently Secure Multiparty Computation},
author = {Oscar G. Bautista and Mohammad Hossein Manshaei and Richard Hernandez and Kemal Akkaya and Soamar Homsi and Selcuk Uluagac},
url = {https://doi.org/10.1007/s10922-023-09739-y},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
journal = {Springer Journal of Network and Systems Management Journal},
publisher = {Plenum Press},
address = {USA},
abstract = {Secure Multiparty Computation (MPC) offers privacy-preserving computation that could be critical in many health and finance applications. Specifically, two or more parties jointly compute a function on private inputs by following a protocol executed in rounds. The MPC network typically consists of direct peer-to-peer (P2P) connections among parties. However, this significantly increases the computation time as parties need to wait for messages from each other, thus making network communication a bottleneck. Most recent works tried to address the communication efficiency by focusing on optimizing the MPC protocol rather than the underlying network topologies and protocols. In this paper, we propose the MPC over Algorand Blockchain (MPC-ABC) protocol that packs messages into Algorand transactions and utilizes its fast gossip protocol to transmit them efficiently among MPC parties. Our approach, therefore},
keywords = {Secure Multipart Computation},
pubstate = {published},
tppubtype = {article}
}
Secure Multiparty Computation (MPC) offers privacy-preserving computation that could be critical in many health and finance applications. Specifically, two or more parties jointly compute a function on private inputs by following a protocol executed in rounds. The MPC network typically consists of direct peer-to-peer (P2P) connections among parties. However, this significantly increases the computation time as parties need to wait for messages from each other, thus making network communication a bottleneck. Most recent works tried to address the communication efficiency by focusing on optimizing the MPC protocol rather than the underlying network topologies and protocols. In this paper, we propose the MPC over Algorand Blockchain (MPC-ABC) protocol that packs messages into Algorand transactions and utilizes its fast gossip protocol to transmit them efficiently among MPC parties. Our approach, therefore
2.
Abbas Acar, Z. Berkay Celik, Hidayet Aksu, A. Selcuk Uluagac, Patrick McDaniel
Achieving Secure and Differentially Private Computations in Multiparty Settings Conference Paper
In the Proceedings of the IEEE Symposium on Privacy-Aware Computing (PAC), 2017.
Abstract | Links | BibTeX | Tags: Cryptojacking, Secure Multipart Computation
@conference{AcarSecureIEEEPAC,
title = {Achieving Secure and Differentially Private Computations in Multiparty Settings},
author = {Abbas Acar and Z. Berkay Celik and Hidayet Aksu and A. Selcuk Uluagac and Patrick McDaniel},
url = {https://patrickmcdaniel.org/pubs/aca17.pdf},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {In the Proceedings of the IEEE Symposium on Privacy-Aware Computing (PAC)},
abstract = {Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others' data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.},
keywords = {Cryptojacking, Secure Multipart Computation},
pubstate = {published},
tppubtype = {conference}
}
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (SMC) is a viable panacea for this, allowing distributed parties to make computations while the parties learn nothing about their data, but the final result. Although SMC is instrumental in such distributed settings, it does not provide any guarantees not to leak any information about individuals to adversaries. Differential privacy (DP) can be utilized to address this; however, achieving SMC with DP is not a trivial task, either. In this paper, we propose a novel Secure Multiparty Distributed Differentially Private (SM-DDP) protocol to achieve secure and private computations in a multiparty environment. Specifically, with our protocol, we simultaneously achieve SMC and DP in distributed settings focusing on linear regression on horizontally distributed data. That is, parties do not see each others' data and further, can not infer information about individuals from the final constructed statistical model. Any statistical model function that allows independent calculation of local statistics can be computed through our protocol. The protocol implements homomorphic encryption for SMC and functional mechanism for DP to achieve the desired security and privacy guarantees. In this work, we first introduce the theoretical foundation for the SM-DDP protocol and then evaluate its efficacy and performance on two different datasets. Our results show that one can achieve individual-level privacy through the proposed protocol with distributed DP, which is independently applied by each party in a distributed fashion. Moreover, our results also show that the SM-DDP protocol incurs minimal computational overhead, is scalable, and provides security and privacy guarantees.
Citations: 8413
h-index: 44
i10-index: 107
