Private Inputs Research Articles

Secure Quantum Private Comparison Protocol Based on the Entanglement Swapping Between Three-Particle W-Class State and Bell State

We propose a new quantum private comparison protocol with the help of a semi-honest third party (TP), enabling two participants to compare the equality of their private inputs without exposing any information about their respective private inputs. Different from previous protocols, our protocol utilizes the properties of entanglement swapping between three-particle W-Class state and Bell state. The presented protocol can ensure correctness, fairness and security. Meanwhile, all the quantum particles undergo a one-way transmission, and all the participants including TP are just required having the ability to perform Bell-state measurement and exclusive-or operation which make our protocol more feasible and efficient. At last, the security of this protocol with respect to various kinds of attacks is analyzed in detail.

Factor substitution is an engine of growth in a model with productive public expenditure

This paper examines the effect of the elasticity of substitution between public and private inputs on the optimal fiscal policy, long-run growth and welfare. To this end, we consider an endogenous growth model with productive public spending. If the baseline government size is suboptimally low (high), the higher the elasticity of substitution the higher (lower) are the optimal government size and, in the presence of congestion, the optimal income tax. In any case, the higher the elasticity of substitution the higher are the optimal long-run growth rate and welfare.

Fast Cut-and-Choose-Based Protocols for Malicious and Covert Adversaries

In the setting of secure two-party computation, two parties wish to securely compute a joint function of their private inputs, while revealing only the output. One of the primary techniques for achieving efficient secure two-party computation is that of Yao's garbled circuits (FOCS 1986). In the semi-honest model, where just one garbled circuit is constructed and evaluated, Yao's protocol has proven itself to be very efficient. However, a malicious adversary who constructs the garbled circuit may construct a garbling of a different circuit computing a different function, and this cannot be detected (due to the garbling). In order to solve this problem, many circuits are sent and some of them are opened to check that they are correct while the others are evaluated. This methodology, called cut-and-choose, introduces significant overhead, both in computation and in communication, and is mainly due to the number of circuits that must be used in order to prevent cheating. In this paper, we present a cut-and-choose protocol for secure computation based on garbled circuits, with security in the presence of malicious adversaries, that vastly improves on all previous protocols of this type. Concretely, for a cheating probability of at most $$2^{-40}$$2-40, the best previous works send between 125 and 128 circuits. In contrast, in our protocol 40 circuits alone suffice (with some additional overhead). Asymptotically, we achieve a cheating probability of $$2^{-s}$$2-s where $$s$$s is the number of garbled circuits, in contrast to the previous best of $$2^{-0.32s}$$2-0.32s. We achieve this by introducing a new cut-and-choose methodology with the property that in order to cheat, all of the evaluated circuits must be incorrect, and not just the majority as in previous works. The security of our protocol relies on the decisional Diffie---Hellman assumption.

A general two‐party bi‐input private function evaluation protocol

AbstractIn the past, researchers have discussed the problem of two‐party single‐input private function evaluation (PFE), where P1 holds a private input x while P2 holds a private circuit Cf, and their goal is to securely compute Cf(x) without revealing x and Cf. Herein, we further consider a more general case, two‐party bi‐input PFE, where P2 also participates in the PFE by contributing a private input y. The research in this general case is of great value not only in theory but also in practice. In this paper, we focus on this problem and propose the first constant‐round two‐party bi‐input PFE protocol, which is with linear complexity and without relying on universal circuit or fully homomorphic encryption. Copyright © 2015 John Wiley & Sons, Ltd.

Multiplicadores Fiscais e Investimento em Infraestrutura

This article develops a dynamic and stochastic general equilibrium model to analyze the different effects of government expenditures based on consumption and on investment for the Brazilian economy. The model includes public capital as a production factor and analyzes the impact of public investment on the marginal productivity of private inputs and on GDP. The analysis is based on present value multipliers, allowing the consideration of the dynamic effects of fiscal policy. The results show the advantages of a fiscal policy based on investment in comparison to a fiscal policy based on consumption expenditures.

Catching up and falling behind

This paper studies the interaction between technology, a public input that flows in from abroad, and human capital, a private input that is accumulated domestically, as twin engines of growth in a developing economy. The model displays two types of long run behavior, depending on policies and initial conditions. One is sustained growth, where the economy keeps pace with the technology frontier. The other is stagnation, where the economy converges to a minimal technology level that is independent of the world frontier. In a calibrated version of the model, transition paths after a policy change can display rapid growth, as in modern growth ‘miracles.’ In these economies policies that promote technology inflows are much more effective than subsidies to human capital accumulation in accelerating growth. A policy reversal produces a ‘lost decade,’ a period of slow growth that permanently reduces the level of income and consumption.

An Efficient Protocol for the Private Comparison of Equal Information Based on Four-Particle Entangled W State and Bell Entangled States Swapping

In order to enable two participants to compare the equality of their information without leaking any information about their respective private inputs, an efficient protocol with the assistance of a semi-honest third party is proposed. Different from previous protocols, this protocol based on four-particle entangled W state and Bell Entangled States swapping. One party cannot learn the other’s private information. And the third party also cannot learn any information about the private information except the comparing results. Last, the correctness of the protocol is analyzed and for proving the security of the protocol, various kinds of outside attacks and participant attacks are discussed.

High-Capacity Quantum Summation with Single Photons in Both Polarization and Spatial-Mode Degrees of Freedom

In this paper, we employ single photons in both polarization and spatial-mode degrees of freedom to design a quantum summation protocol. We assume that the third party, i.e. TP, is semi-honest in our protocol. That TP is semi-honest means TP executes the protocol loyally, keeps a record of all its intermediate computations and might try to steal the participants’ private inputs from the record, but he cannot be corrupted by the adversary. Participants can independently encode their private inputs on the polarization states and the spatial-mode states of single photons. Thus our protocol doubles the capacity of quantum communication compared with those based on single photons with only one degree of freedom. In addition, our protocol is feasible as the preparation and the measurement of single-photon quantum states in both the polarization and the spatial-mode degrees of freedom are available with current quantum techniques. We also analyze its security in this paper.

Constant-round adaptive zero-knowledge proofs for NP

Secure two-party computation allows two parties with private inputs to securely compute some function of their inputs, even in the presence of a malicious adversary. In this work, we revisit zero-knowledge proofs and focus on adaptive adversaries, which could corrupt an arbitrary number of parties and adaptively determine who and when to corrupt during the computation phase.Previous constructions could realize adaptive zero-knowledge proofs for all languages in NP (Lindell and Zarosim TCC’09) at the cost of a high round-complexity, i.e., super-constant number of rounds. In this work, assuming the existence of constant-round statistically hiding commitment schemes, we build efficient adaptive zero-knowledge proofs for all languages in NP, which only require constant number of communication rounds. The system is also a proof of knowledge. The construction relies on an adaptive instance-dependent commitment scheme, and the proof of security requires only the use of black-box techniques and is presented according to the real/ideal simulation paradigm.

Oblivious decision program evaluation

In this study, the authors design efficient protocols for a number of ‘oblivious decision program (DP) evaluation’ problems. Consider a general form of the problem where a client who holds a private input interacts with a server who holds a private DP (e.g. a decision tree or a branching program) with the goal of evaluating his input on the DP without learning any additional information. Many known private database query problems such as symmetric private information retrieval and private keyword search can be formulated as special cases of this problem. Most of the existing works on the same problem focus on optimising communication. However, in some environments (supported by a few experimental studies), it is the computation and not the communication that may be the performance bottleneck. In this study, we design ‘computationally efficient’ protocols for the above general problem, and a few of its special cases. In addition to being one-round and requiring a small amount of work by the client (in the RAM model), the proposed protocols only require a small number of exponentiations (independent of the server's input) by both parties. The proposed constructions are, in essence, efficient and black-box reductions of the above problem to 1-out-of-2 oblivious transfer. It is proved that the proposed protocols secure (private) against ‘malicious’ adversaries in the standard ideal/real-world simulation-based paradigm.

Incentive Compatible Privacy-Preserving Data Analysis

In many cases, competing parties who have private data may collaboratively conduct privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. Most often, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. Unless proper incentives are set, current PPDA techniques cannot prevent participating parties from modifying their private inputs.incentive compatible privacy-preserving data analysis techniques This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful inputs. In this paper, we first develop key theorems, then base on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party.

Secure computation under network and physical attacks

Secure computation enables many parties to jointly compute a function of their private inputs. The security requirement is that the input privacy of any honest party is preserved even if other parties participating in the protocol collude or deviate from the protocol. In concurrent and physical adversarial parties try to break the privacy of honest parties by exploiting the network connection or physical weaknesses of the honest parties’ machine. This article provides an overview of the results for achieving secure computation in presence of concurrent and physical attacks contained in the phD thesis:”Secure Computation under concurrent and physical attacks, with emphasis to the relation of such results with the state of the art.

A systematic approach to practically efficient general two-party secure function evaluation protocols and their modular design

General two-party Secure Function Evaluation (SFE) allows mutually distrusting parties to correctly compute any function on their private input data, without revealing the inputs. Two-party SFE can benefit almost any client-server interaction where p

Government investment and the stock market

High rates of government investment in public sector capital forecast high risk premiums both at the aggregate and firm-level. This result is in sharp contrast with the well-documented negative relationship between the private sector investment rate and risk premiums. To explain the empirical findings, we extend the neoclassical q-theory model of investment and specify public sector capital as an additional input in the firm's technology. We show that the model can quantitatively replicate the empirical facts with reasonable parameter values if public sector capital increases the marginal productivity of private inputs.

A new privacy-preserving Euclid-distance protocol and its applications in WSNs

Recently, privacy concerns become an increasingly critical issue. Secure multi-party computation plays an important role in privacy-preserving. Secure multi-party computational geometry is a new field of secure multi-party computation. In this paper, we devote to investigating the solutions to some secure geometric problems in a cooperative environment. The problem is collaboratively computing the Euclid-distance between two private vectors without disclosing the private input to each other. A general privacy-preserving Euclid-distance protocol is firstly presented as a building block and is proved to be secure and efficient in the comparison with the previous methods. And we proposed a new protocol for the application in Wireless Sensor Networks (WSNs), based on the novel Euclid-distance protocol and Density-Based Clustering Protocol (DBCP), so that the nodes from two sides can compute cooperatively to divide them into clusters without disclosing their location information to the opposite side.

A Secure Scalar Product Protocol and Its Applications to Computational Geometry

A secure scalar product protocol is a type of specific SMC problem, and has found various applications in many areas such as privacy-preserving data mining, privacy-preserving cooperative statistical analysis, and privacy-preserving geometry computation. In this paper, we firstly extend to a solution of homomorphic-encryption based secure scalar product protocol such that it enables the scheme to be used in distributed decryption, and to deal with negative vectors. Secondly, we propose two-party secure computation of a public Boolean function on private inputs of each party. Thirdly, we describe two applications of our secure scalar product protocol to computational geometry: determining securely location of a point to a directed line segment, and conditional oblivious transfer based on the relation between a private point and a private directed line.

Distributed RK- Secure Sum Protocol for Privacy Preserving

Secure multi party computation allows several parties to compute some function of their inputs without disclosing the actual input to one another. Secure sum computation is an easily understood example and the component of the various secure multi party computation solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a protocol with more security, when a group of the computing parties want to know the data of some other party. I. INTRODUCTION The huge growth of the Internet and its easy access by common man created opportunities for joint computations by multiple parties. All the participating parties for the sake of their mutual benefit want to compute the common function of their inputs but at the same time they are worried about the privacy of their data. This subject of the information security is called secure multi party computation. This subject has two goals; one is the privacy of the individual data inputs and another is the correctness of the result. Mainly two models exist in the literature for the analysis of the secure multi party computation problems. Ideal model of the secure multi party computation uses a Trusted Third Party apart from the participating parties. Parties supply their inputs to the Trusted Third Party. Computation of the function is done by the Trusted Third Party and then the result is sent to all the parties. In this paradigm the trustworthiness of the Trusted Third Party is critically important because if the Trusted Third Party turns corrupt, it can supply the private inputs of one party to others. But it is extensively used model of the secure multi party computation due to its easy implementation and the protocols available which prevent the Trusted Third Party to act maliciously. Real model of the secure multi party computation does not use any Trusted Third Party but the parties themselves agree on some protocol for the computation. The parties behavior in the secure multi party computation is important to consider. An honest party follows the protocol and respects the privacy of other parties. A semi honest party follows the protocol but also tries to learn other information than the result. The corrupt party neither follows the protocol nor respects the privacy of other parties. Different protocols are needed for different secure multi party computation models and the behavior of the party. Solutions are available for secure multi party computation problems using cryptographic techniques, randomization techniques and anonymization methods. The subject of secure multi party computation has been evolved from two party comparison problems (1) to multiparty image template matching problems. Many specific secure multi party computation problems have been defined and analyzed by researchers like Private Information Retrieval, Selective Function Evaluation, Privacy-Preserving Database Query, Privacy-Preserving Geometric Computation, Privacy- Preserving Statistical Analysis, Privacy-Preserving Intrusion Detection and Privacy-Preserving Cooperative Scientific Computation. Based on these general secure multi party computation problems many real life applications have been emerged like Privacy- Preserving Electronic Voting, Privacy-Preserving Bidding and Auctions, Privacy-Preserving Social Network Analysis, Privacy-Preserving Signature and Face Detection, Etc. Secure sum computation problem of secure multi party computation can be defined as: How multiple parties can compute the sum of their input values without disclosing actual values to one another. Secure sum can work as the tool for the secure multi party computation solutions in the privacy preserving distributed data mining problems (2). We proposed a secure sum protocol using random numbers (2). In this paper, we proposed a novel changing neighbors approach Distributed RK secure sum protocol for achieving more security in case a group of the parties collude to know the private data of some other party. 1.1 Distributed Database Partition: - Database is divided into three main portioning horizontal partition, vertical partition and hybrid partition. 1.1.1 Horizontal Partitioning: - Horizontal partitioning divides the whole database into the number of small database according to the row splitting. So that the execution of query will be very fast as well as we will be able to provide more privacy to the portioned database.

Catching Up and Falling Behind

This paper studies the interaction between technology, a publicly available input that flows in from abroad, and human capital, a private input that is accumulated domestically, as the twin engines of growth in a developing economy. The model displays two types of long run behavior, depending on policies and initial conditions. One is sustained growth, where the economy keeps pace with the technology frontier. The other is stagnation, where the economy converges to a minimal technology level that is independent of the world frontier.In a calibrated version of the model, transition paths after a policy change can display rapid growth, as in modern growth 'miracles.' In these economies policies that promote technology inflows are much more effective than subsidies to human capital accumulation in accelerating growth. A policy reversal produces a 'lost decade,' a period of slow growth that permanently reduces the level of income and consumption.

Quantum Private Comparison Protocol Based on Cluster States

We present a quantum private comparison (QPC) protocol, enabling two players to compare the equality of their information without revealing any information about their respective private inputs, in which the four-particle cluster states as the information carriers are used. The presented protocol can ensure correctness, privacy, and fairness with the assistance of a semi-trusted third party (TP). Meanwhile, the participants including the TP are just required having the ability to perform single-particle measurements, which make the presented protocol more feasible in technique. Furthermore, the photon transmission is a one-way distribution; the Trojan horse attacks can be automatically avoided. The security of this protocol is also analyzed.

Government Investment and the Stock Market

High rates of government investment in public sector capital forecast high risk premiums both at the aggregate and firm-level. This result is in sharp contrast with the well-documented negative relationship between the private sector investment rate and risk premiums. To explain the empirical findings, we extend the neoclassical q-theory model of investment and specify public sector capital as an additional input in the firm’s technology. We show that the model can quantitatively replicate the empirical facts with reasonable parameter values if public sector capital increases the marginal productivity of private inputs.