Threshold signature has been widely used in electronic wills,electronic elections,cloud computing,secure multiparty computation and other fields.Until now,certificateless threshold signature schemes are all based on t...Threshold signature has been widely used in electronic wills,electronic elections,cloud computing,secure multiparty computation and other fields.Until now,certificateless threshold signature schemes are all based on traditional mathematic theory,so they cannot resist quantum computing attacks.In view of this,we combine the advantages of lattice-based cryptosystem and certificateless cryptosystem to construct a certificateless threshold signature from lattice(LCLTS)that is efficient and resistant to quantum algorithm attacks.LCLTS has the threshold characteristics and can resist the quantum computing attacks,and the analysis shows that it is unforgeable against the adaptive Chosen-Message Attacks(UF-CMA)with the difficulty of Inhomogeneous Small Integer Solution(ISIS)problem.In addition,LCLTS solves the problems of the certificate management through key escrow.展开更多
The paper proposes the optimization problem of maximizing the sum of suBmodular and suPermodular(BP)functions with partial monotonicity under a streaming fashion.In this model,elements are randomly released from the s...The paper proposes the optimization problem of maximizing the sum of suBmodular and suPermodular(BP)functions with partial monotonicity under a streaming fashion.In this model,elements are randomly released from the stream and the utility is encoded by the sum of partial monotone suBmodular and suPermodular functions.The goal is to determine whether a subset from the stream of size bounded by parameter k subject to the summarized utility is as large as possible.In this work,a threshold-based streaming algorithm is presented for the BP maximization that attains a(1-k)/(2-k)-O(e)-approximation with O(1/e^(4)1og^(3)(1/s)log(2-k)k/(1-k)^(2))memory complexity,where k denotes the parameter of supermodularity ratio.We further consider a more general model with fair constraints and present a greedy-based algorithm that obtains the same approximation.We finally investigate this fair model under the streaming fashion and provide a(1-k)^(4)/(2-2k+k^(2))^(2)-O(e)-approximation algorithm.展开更多
Recent progress in maximizing submodular functions with a cardinality constraint through centralized and streaming modes has demonstrated a wide range of applications and also developed comprehensive theoretical guara...Recent progress in maximizing submodular functions with a cardinality constraint through centralized and streaming modes has demonstrated a wide range of applications and also developed comprehensive theoretical guarantees.The submodularity was investigated to capture the diversity and representativeness of the utilities,and the monotonicity has the advantage of improving the coverage.Regularized submodular optimization models were developed in the latest studies(such as a house on fire),which aimed to sieve subsets with constraints to optimize regularized utilities.This study is motivated by the setting in which the input stream is partitioned into several disjoint parts,and each part has a limited size constraint.A first threshold-based bicriteria(1/3,2/3/)-approximation for the problem is provided.展开更多
Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniq...Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.展开更多
基金supported by the Key Project of Natural Science Basic Research Plan of Shaanxi Province under the Grant 2020JZ-54.
文摘Threshold signature has been widely used in electronic wills,electronic elections,cloud computing,secure multiparty computation and other fields.Until now,certificateless threshold signature schemes are all based on traditional mathematic theory,so they cannot resist quantum computing attacks.In view of this,we combine the advantages of lattice-based cryptosystem and certificateless cryptosystem to construct a certificateless threshold signature from lattice(LCLTS)that is efficient and resistant to quantum algorithm attacks.LCLTS has the threshold characteristics and can resist the quantum computing attacks,and the analysis shows that it is unforgeable against the adaptive Chosen-Message Attacks(UF-CMA)with the difficulty of Inhomogeneous Small Integer Solution(ISIS)problem.In addition,LCLTS solves the problems of the certificate management through key escrow.
基金supported by the National Natural Science Foundation of China(No.12101587)the China Postdoctoral Science Foundation(No.2022M720329)+2 种基金the National Natural Science Foundation of China(No.12001523)the Beijing Natural Science Foundation Project(No.Z200002)the National Natural Science Foundation of China(No.12131003).
文摘The paper proposes the optimization problem of maximizing the sum of suBmodular and suPermodular(BP)functions with partial monotonicity under a streaming fashion.In this model,elements are randomly released from the stream and the utility is encoded by the sum of partial monotone suBmodular and suPermodular functions.The goal is to determine whether a subset from the stream of size bounded by parameter k subject to the summarized utility is as large as possible.In this work,a threshold-based streaming algorithm is presented for the BP maximization that attains a(1-k)/(2-k)-O(e)-approximation with O(1/e^(4)1og^(3)(1/s)log(2-k)k/(1-k)^(2))memory complexity,where k denotes the parameter of supermodularity ratio.We further consider a more general model with fair constraints and present a greedy-based algorithm that obtains the same approximation.We finally investigate this fair model under the streaming fashion and provide a(1-k)^(4)/(2-2k+k^(2))^(2)-O(e)-approximation algorithm.
基金This work was supported by the Beijing Natural Science Foundation Project(No.Z200002)the National Natural Science Foundation of China(Nos.12001523,12131003,and 12101587)+1 种基金the National Innovation and Entrepreneurship Training Program for College Students of Beijing University of Technology(No.GJDC-2022-01-39)the China Postdoctoral Science Foundation(No.2022M720329).
文摘Recent progress in maximizing submodular functions with a cardinality constraint through centralized and streaming modes has demonstrated a wide range of applications and also developed comprehensive theoretical guarantees.The submodularity was investigated to capture the diversity and representativeness of the utilities,and the monotonicity has the advantage of improving the coverage.Regularized submodular optimization models were developed in the latest studies(such as a house on fire),which aimed to sieve subsets with constraints to optimize regularized utilities.This study is motivated by the setting in which the input stream is partitioned into several disjoint parts,and each part has a limited size constraint.A first threshold-based bicriteria(1/3,2/3/)-approximation for the problem is provided.
文摘Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.
