Private Set Intersection (PSI) is a vital cryptographic technique used for securely computing common data of different sets. In PSI protocols, often two parties hope to find their common set elements without needing to disclose their uncommon ones. In recent years, the cloud has been playing an influential role in PSI protocols which often need huge computational tasks. In 2017, Abadi et al. introduced a scheme named EO-PSI which uses a cloud to pass on the main computations to it and does not include any public-key operations. In EO-PSI, parties need to set up secure channels beforehand; otherwise, an attacker can easily eavesdrop on communications between honest parties and find private information. This paper presents an improved EO-PSI scheme which has the edge on the previous scheme in terms of privacy and complexity. By providing possible attacks on the prior scheme, we show the necessity of using secure channels between parties. Also, our proposed protocol is secure against passive attacks without having to have any secure channels. We measure the protocol's overhead and show that computational complexity is considerably reduced and also is fairer compared to the previous scheme.
The engineering challenges involved in building large scale quantum computers, and the associated infrastructure requirements, mean that when such devices become available it is likely that this will be in limited numbers and in limited geographic locations. It is likely that many users will need to rely on remote access to delegate their computation to the available hardware. In such a scenario, the privacy and reliability of the delegated computations are important concerns. On the other hand, the distributed nature of modern computations has led to a widespread class of applications in whic…
Timely sharing of electronic health records (EHR) across providers is essential and significance in facilitating medical researches and prompt patients' care. With sharing, it is crucial that patients can control who can access their data and when, and guarantee the security and privacy of their data. In current literature, various system models, cryptographic techniques and access control mechanisms are proposed which requires patient's consent before sharing. However, they mostly focus on patient is available to authorize the access of the EHR upon requested. This is impractical given that t…
Sensitive computations are now routinely delegated to third-parties. In response, Confidential Computing technologies are being introduced to microprocessors, offering a protected processing environment, which we generically call an isolate, providing confidentiality and integrity guarantees to code and data hosted within -- even in the face of a privileged attacker. Isolates, with an attestation protocol, permit remote third-parties to establish a trusted "beachhead" containing known code and data on an otherwise untrusted machine. Yet, the rise of these technologies introduces many new probl…
We introduce a secure hardware device named a QEnclave that can secure the remote execution of quantum operations while only using classical controls. This device extends to quantum computing the classical concept of a secure enclave which isolates a computation from its environment to provide privacy and tamper-resistance. Remarkably, our QEnclave only performs single-qubit rotations, but can nevertheless be used to secure an arbitrary quantum computation even if the qubit source is controlled by an adversary. More precisely, attaching a QEnclave to a quantum computer, a remote client control…
In the current work we discuss the notion of gateways as a means for interoperability across different blockchain systems. We discuss two key principles for the design of gateway nodes and scalable gateway protocols, namely (i) the opaque ledgers principle as the analogue of the autonomous systems principle in IP datagram routing, and (ii) the externalization of value principle as the analogue of the end-to-end principle in the Internet architecture. We illustrate the need for a standard gateway protocol by describing a unidirectional asset movement protocol between two peer gateways, under th…
Private Delegated Quantum Computing for User-Level and Industry-Level Settings
Instituto Tecnológico de Castilla y León（卡斯蒂利亚和莱昂理工学院）；National University of San Marcos（圣马科斯国立大学）；Universidad Nacional del Comahue（科马韦国立大学）；Thinkcomm SRL（Thinkcomm有限公司）
Delegating difficult computations to remote large computation facilities, with appropriate security guarantees, is a possible solution for the ever-growing needs of personal computing power. For delegated computation protocols to be usable in a larger context---or simply to securely run two protocols in parallel---the security definitions need to be composable. Here, we define composable security for delegated quantum computation. We distinguish between protocols which provide only blindness---the computation is hidden from the server---and those that are also verifiable---the client can check…
In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi…
Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrustworthiness of the service providers are obstacles of making the database as a service.Collection and storage of personally identifiable information is a major privacy concern.On-line public databases and resources pose a significant risk to user privacy, since a malicious database owner may monitor user queries and infer useful information about the customer.The challenge in data privacy is to share data with third-party and at the same time securing the valuable information from unaut…
Growth in research collaboration has caused an increased need for sharing of data. However, when this data is private, there is also an increased need for maintaining security and privacy. Secure multi-party computation enables any function to be securely evaluated over private data without revealing any unintended data. A number of tools and compilers have been recently developed to support evaluation of various functionalities over private data. PICCO is one of such compilers that transforms a general-purpose user program into its secure distributed implementation. Here we assess performance…
Cloud computing emerges as an attractive solution that can be delegated to store and process confidential data. However, several security risks are encountered with such a system as the securely encrypted data should be decrypted before processing them. Therefore, the decrypted data is susceptible to reading and alterations. As a result, processing encrypted data has been a research subject since the publication of the RSA encryption scheme in 1978. In this paper we present a relational database system based on homomorphic encryption schemes to preserve the integrity and confidentiality of the…
Blockchain is increasingly being used to provide a distributed, secure, trusted, and private framework for energy trading in smart grids. However, existing solutions suffer from lack of privacy, processing and packet overheads, and reliance on Trusted Third Parties (TTP). To address these challenges, we propose a Secure Private Blockchain-based (SPB) framework. SPB enables the energy producers and consumers to directly negotiate the energy price. To reduce the associated packet overhead, we propose a routing method which routes packets based on the destination Public Key (PK). SPB eliminates t…
Performing machine learning tasks in mobile applications yields a challenging conflict of interest: highly sensitive client information (e.g., speech data) should remain private while also the intellectual property of service providers (e.g., model parameters) must be protected. Cryptographic techniques offer secure solutions for this, but have an unacceptable overhead and moreover require frequent network interaction. In this work, we design a practically efficient hardware-based solution. Specifically, we build Offline Model Guard (OMG) to enable privacy-preserving machine learning on the pr…
Preserving the privacy of individual databases when carrying out statistical calculations has a long history in statistics and had been the focus of much recent attention in machine learning In this paper, we present a protocol for computing logistic regression when the data are held by separate parties without actually combining information sources by exploiting results from the literature on multi-party secure computation. We provide only the final result of the calculation compared with other methods that share intermediate values and thus present an opportunity for compromise of values in …
Homomorphic encryption is a form of encryption which allows computation to be carried out on the encrypted data without the need for decryption. The success of quantum approaches to related tasks in a delegated computation setting has raised the question of whether quantum mechanics may be used to achieve information theoretically secure fully homomorphic encryption. Here we show, via an information localisation argument, that deterministic fully homomorphic encryption necessarily incurs exponential overhead if perfect security is required.
In this paper, we propose a very secure and reliable owner-self-managed private key recovery method. In recent years, Public Key Authentication (PKA) method has been identified as the most feasible online security solution. However, losing the private key also implies the risk of losing the ownership of the assets associated with the private key. For key protection, the commonly adopted something-you-x solutions require a new secret to protect the target secret and fall into a circular protection issue as the new secret has to be protected too. To resolve the circular protection issue and prov…
In a Private section intersection (PSI) protocol, Alice and Bob compute the intersection of their respective sets without disclosing any element not in the intersection. PSI protocols have been extensively studied in the literature and are deployed in industry. With state-of-the-art protocols achieving optimal asymptotic complexity, performance improvements are rare and can only improve complexity constants. In this paper, we present a new private, extremely efficient comparison protocol that leads to a PSI protocol with low constants. A useful property of our comparison protocol is that it ca…
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 t…
We model a private key`s life cycle as a finite state machine. The states are the key`s phases of life and the transition functions describe tasks to be done with the key. Based on this we define and describe the key authority, a trust center module, which potentiates the easy enforcement of secure management of private keys in hierarchical public key infrastructures. This is done by assembling all trust center tasks concerning the crucial handling of private keys within one centralized module. As this module resides under full control of the trust center`s carrier it can easily be protected b…
Secure sum computation of private data inputs is an interesting example of Secure Multiparty Computation (SMC) which has attracted many researchers to devise secure protocols with lower probability of data leakage. In this paper, we provide a novel protocol to compute the sum of individual data inputs with zero probability of data leakage when two neighbor parties collude to know the data of a middle party. We break the data block of each party into number of segments and redistribute the segments among parties before the computation. These entire steps create a scenario in which it becomes im…
Secure sum computation of private data inputs is an important component of Secure Multi party Computation (SMC).In this paper we provide a protocol to compute the sum of individual data inputs with zero probability of data leakage. In our proposed protocol we break input of each party into number of segments and change the arrangement of the parties such that in each round of the computation the neighbors are changed. In this protocol it becomes impossible for semi honest parties to know the private data of some other party.
Raziel combines secure multi-party computation and proof-carrying code to provide privacy, correctness and verifiability guarantees for smart contracts on blockchains. Effectively solving DAO and Gyges attacks, this paper describes an implementation and presents examples to demonstrate its practical viability (e.g., private and verifiable crowdfundings and investment funds). Additionally, we show how to use Zero-Knowledge Proofs of Proofs (i.e., Proof-Carrying Code certificates) to prove the validity of smart contracts to third parties before their execution without revealing anything else. Fi…
Recently, Yang and Tan proposed a certificateless key exchange protocol without pairing, and claimed their scheme satisfies forward secrecy, which means no adversary could derive an already-established session key unless the full user secret keys (including a private key and an ephemeral secret key) of both communication parties are compromised. However, in this paper, we point out their protocol is actually not secure as claimed by presenting an attack launched by an adversary who has learned the private key of one party and the ephemeral secret key of the other, but not the full user secret …
The Enterprise Ethereum Client Specification by the Enterprise Ethereum Alliance defines the requirements which Ethereum Clients offering private smart contract capabilities should comply with. This specification though ground breaking, misses some important blockchain requirements and does not fully consider the requirements of Ethereum Clients offering Private Sidechain capabilities. This paper presents the case for Private Sidechains and defines requirements to be complied with to deliver this technology. The capabilities of three blockchain clients have been analysed based on the requireme…
We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and practitioners, including tight integration with ordinary machine learning processes, existing optimizations for distributed computation in TensorFlow, high-level abstractions for expressing complex algorithms and protocols, and an expanded set of familiar tooling. We give an open source implementation of a state-of-the-art protocol and report on concrete benchmarks using typical models from private machine learning.
Recent compilers allow a general-purpose program (written in a conventional programming language) that handles private data to be translated into secure distributed implementation of the corresponding functionality. The resulting program is then guaranteed to provably protect private data using secure multi-party computation techniques. The goals of such compilers are generality, usability, and efficiency, but the complete set of features of a modern programming language has not been supported to date by the existing compilers. In particular, recent compilers PICCO and the two-party ANSI C com…
Secure information retrieval is an essential task in today's highly digitised society. In some applications, it may be necessary that user query's privacy and database content's security are enforced. For these settings, symmetric private information retrieval (SPIR) could be employed, but its implementation is known to be demanding, requiring a private key-exchange network as the base layer. Here, we report for the first time a realisation of provably-secure SPIR supported by a quantum-secure key-exchange network. The SPIR scheme looks at biometric security, offering secure retrieval of 582-b…
Recent work has highlighted the risks of intellectual property (IP) piracy of deep learning (DL) models from the side-channel leakage of DL hardware accelerators. In response, to provide side-channel leakage resiliency to DL hardware accelerators, several approaches have been proposed, mainly borrowed from the methodologies devised for cryptographic implementations. Therefore, as expected, the same challenges posed by the complex design of such countermeasures should be dealt with. This is despite the fact that fundamental cryptographic approaches, specifically secure and private function eval…
We present Private Data Objects (PDOs), a technology that enables mutually untrusted parties to run smart contracts over private data. PDOs result from the integration of a distributed ledger and Intel Secure Guard Extensions (SGX). In particular, contracts run off-ledger in secure enclaves using Intel SGX, which preserves data confidentiality, execution integrity and enforces data access policies (as opposed to raw data access). A distributed ledger verifies and records transactions produced by PDOs, in order to provide a single authoritative instance of such objects. This allows contracting …
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with RMSprop, which combines differential privacy and homomorphic encryption in a harmonious manner while protecting private information. We applied the proposed secure and privacy-preserving distributed Bayesian learning a…
University of Vermont（佛蒙特大学）；MassMutual（万通互惠）；Raytheon（雷神公司）
Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges -- many require a trusted third party or else add too much noise to produce useful models. Recent advances in \emph{secure aggregation} using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially…
We present three private fingerprint alignment and matching protocols, based on what are considered to be the most precise and efficient fingerprint recognition algorithms, which use minutia points. Our protocols allow two or more honest-but-curious parties to compare their respective privately-held fingerprints in a secure way such that they each learn nothing more than an accurate score of how well the fingerprints match. To the best of our knowledge, this is the first time fingerprint alignment based on minutiae is considered in a secure computation framework. We build secure fingerprint al…
This paper reports on the development and evaluation of a prototype blockchain solution for private and secure individual omics health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful user interface design of …
In an electronic auction protocol, the main participants are the seller, a set of trusted auctioneer(s) and the set of bidders. In this paper we consider the situation where there is a seller and a set of n bidders intending to come to an agreement on the selling price of a certain good. Full private or bidder-resolved auction means that this agreement is reached without the help of trusted parties or auctioneers. Therefore, only the seller and the set of bidders are involved, the role of the auctioneers becomes obsolete in this case. property.We propose a new technique for the design of a ful…
We introduce a protocol between a classical polynomial-time verifier and a quantum polynomial-time prover that allows the verifier to securely delegate to the prover the preparation of certain single-qubit quantum states. The protocol realizes the following functionality, with computational security: the verifier chooses one of the observables $Z$, $X$, $Y$, $(X+Y)/\sqrt{2}$, $(X-Y)/\sqrt{2}$; the prover receives a uniformly random eigenstate of the observable chosen by the verifier; the verifier receives a classical description of that state. The prover is unaware of which state he received a…
Providing functionalities that allow online social network users to manage in a secure and private way the publication of their information and/or resources is a relevant and far from trivial topic that has been under scrutiny from various research communities. In this work, we provide a framework that allows users to define highly expressive access policies to their resources in a way that the enforcement does not require the intervention of a (trusted or not) third party. This is made possible by the deployment of a newly defined cryptographic primitives that provides - among other things - …
Microsoft Research（微软研究院）；Algorand Foundation（Algorand 基金会）；Technion（以色列理工学院）；Princeton University（普林斯顿大学）；UC Berkeley（加州大学伯克利分校）
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages - (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secur…
Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation of one party's private model using another party's private data, etc. Although a range of studies implement machine-learning models via secure MPC, such implementations are not yet mainstream. Adoption of secure MPC is hampered by the absence of flexible software frameworks that "speak the language" …
