This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works ...This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network.展开更多
With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inev...With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.展开更多
Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purel...Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.展开更多
Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability an...Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.展开更多
Existing commitment schemes were addressed under the classic two-party scenario, However, popularity of the secure multi-party computation in today＇s lush network communication is motivating us to adopt more sophisti...Existing commitment schemes were addressed under the classic two-party scenario, However, popularity of the secure multi-party computation in today＇s lush network communication is motivating us to adopt more sophisticate commitment schemes. In this paper, we study for the first time multireceiver commitment in unconditionally secure setting, i.e., one committer promises a group of verifiers a common secret value （in computational setting it is trivial）. We extend the Rivest model for this purpose and present a provably secure generic construction using multireceiver authentication codes （without secrecy） as building blocks. Two concrete schemes are proposed as its immediate implementations, which are almost as efficient as an optimal MRA-code. We believe using other primitives to construct variants of this concept will open doors for more interesting research.展开更多
基金the National Natural Science Foundation of China(No.60803146)
文摘This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network.
基金supported in part by the National Key Research and Development Program of China 2020YFB2104500.
文摘With the increasing development of smart grid,multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems.Traditionally,data exchange among parties is inevitable,rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue.This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation(SMPC)with secret sharing protocols.The framework decomposes the collaborative optimization problem into a master problem and several subproblems.The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents.The relationships of agents are completely equal,and there is no privileged agent or any third party.The process of solving subproblems is conducted by agents individually.Compared to the traditional distributed optimization framework,the proposed SMPC-based framework can fully preserve individual private information.Exchanged data among agents are encrypted and no private information disclosure is assured.Furthermore,the framework maintains a limited and acceptable increase in computational costs while guaranteeing opti-mality.Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.
基金the National Key Research and Development Program of China(Grant No.2018YFB0804105)in part by the National Natural Science Foundation of China(Grant Nos.62102037,61932019).
文摘Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.
基金This work was supported in part by the National Key Research and Development Project of China(Grant No.2020YFA0712300)in part by the National Natural Science Foundation of China(Grant Nos.62132005,61632012,62172162 and 62072404).
文摘Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.
基金Supported by the Foundation of Development and Reform Commission of China under Grant High-Tech ([2007] 2367)
文摘Existing commitment schemes were addressed under the classic two-party scenario, However, popularity of the secure multi-party computation in today＇s lush network communication is motivating us to adopt more sophisticate commitment schemes. In this paper, we study for the first time multireceiver commitment in unconditionally secure setting, i.e., one committer promises a group of verifiers a common secret value （in computational setting it is trivial）. We extend the Rivest model for this purpose and present a provably secure generic construction using multireceiver authentication codes （without secrecy） as building blocks. Two concrete schemes are proposed as its immediate implementations, which are almost as efficient as an optimal MRA-code. We believe using other primitives to construct variants of this concept will open doors for more interesting research.
