KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud serve...KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud servers,which makes the search processing a challenging task.In this paper,we focus on the problem of KNN set similarity search over the encrypted datasets.We use Yao’s garbled circuits and secret sharing as underlying tools.To achieve better querying efficiency,we construct a secure R-Tree index structure based on a novel secure grouping protocol,which enables grouping appropriate private values in an oblivious way.Along with several elaborately designed secure arithmetic subroutines,we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds.Theoretically,we analyze the complexity of our schemes in detail,and prove the security in the presence of semi-honest adversaries.Finally,we evaluate the performance and feasibility of our proposed methods by extensive experiments.展开更多
The literature on income smoothing focuses on the effect of earnings smoothing on the equity market.This paper investigates the effect of income smoothing on the debt market.Using the Tucker–Zarowin(TZ) statistic of ...The literature on income smoothing focuses on the effect of earnings smoothing on the equity market.This paper investigates the effect of income smoothing on the debt market.Using the Tucker–Zarowin(TZ) statistic of income smoothing,we find that firms with higher income smoothing rankings exhibit lower cost of debt,suggesting that the information signaling effect of income smoothing dominates the garbling effect.We also find that the effect of earnings smoothing on debt cost reduction is stronger in firms with more opaque information and greater distress risk.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
In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically se...In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements： 1） protocols are information-theoretically t-private for honest but curious parties; 2） the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3） the computational requirements for participating parties are very low： only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t ＋ 1 parties, and Finally, we address the problem of bid changes in an auction. a more randomness efficient protocol for （t ＋ 1）^2 parties展开更多
基金This work was supported by the Natural Science Foundation of China(61602400)Jiangsu Provincial Department of Education(16KJB520043).
文摘KNN set similarity search is a foundational operation in various realistic applications in cloud computing.However,for security consideration,sensitive data will always be encrypted before uploading to the cloud servers,which makes the search processing a challenging task.In this paper,we focus on the problem of KNN set similarity search over the encrypted datasets.We use Yao’s garbled circuits and secret sharing as underlying tools.To achieve better querying efficiency,we construct a secure R-Tree index structure based on a novel secure grouping protocol,which enables grouping appropriate private values in an oblivious way.Along with several elaborately designed secure arithmetic subroutines,we propose an efficient secure and verifiable KNN set similarity search framework over outsourced clouds.Theoretically,we analyze the complexity of our schemes in detail,and prove the security in the presence of semi-honest adversaries.Finally,we evaluate the performance and feasibility of our proposed methods by extensive experiments.
文摘The literature on income smoothing focuses on the effect of earnings smoothing on the equity market.This paper investigates the effect of income smoothing on the debt market.Using the Tucker–Zarowin(TZ) statistic of income smoothing,we find that firms with higher income smoothing rankings exhibit lower cost of debt,suggesting that the information signaling effect of income smoothing dominates the garbling effect.We also find that the effect of earnings smoothing on debt cost reduction is stronger in firms with more opaque information and greater distress risk.
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.
文摘In most of the auction systems the values of bids are known to the auctioneer. This allows him to manipulate the outcome of the auction. Hence, one might be interested in hiding these values. Some cryptographically secure protocols for electronic auctions have been presented in the last decade. Our work extends these protocols in several ways. On the basis of garbled circuits, i.e., encrypted circuits, we present protocols for sealed-bid auctions that fulfill the following requirements： 1） protocols are information-theoretically t-private for honest but curious parties; 2） the number of bits that can be learned by malicious adversaries is bounded by the output length of the auction; 3） the computational requirements for participating parties are very low： only random bit choices and bitwise computation of the XOR-function are necessary. Note that one can distinguish between the protocol that generates a garbled circuit for an auction and the protocol to evaluate the auction. In this paper we address both problems. We will present a t-private protocol for the construction of a garbled circuit that reaches the lower bound of 2t ＋ 1 parties, and Finally, we address the problem of bid changes in an auction. a more randomness efficient protocol for （t ＋ 1）^2 parties
