168 research outputs found

    Efficient secure comparison in the dishonest majority model

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    Secure comparison (SC) is an essential primitive in Secure Multiparty Computation (SMC) and a fundamental building block in Privacy-Preserving Data Analytics (PPDA). Although secure comparison has been studied since the introduction of SMC in the early 80s and many protocols have been proposed, there is still room for improvement, especially providing security against malicious adversaries who form the majority among the participating parties. It is not hard to develop an SC protocol secure against malicious majority based on the current state-of-the-art SPDZ framework. SPDZ is designed to work for arbitrary polynomially-bounded functionalities; it may not provide the most efficient SMC implementation for a specific task, such as SC. In this thesis, we propose a novel and efficient compiler specifically designed to convert most existing SC protocols with semi-honest security into the ones secure against the dishonest majority (malicious majority). We analyze the security of the proposed solutions using the real-ideal paradigm. Moreover, we provide computation and communication complexity analysis. Comparing to the current state-of-the-art SC protocols Rabbit and edaBits, our design offers significant performance gain. The empirical results show that the proposed solution is at least 5 and 10 times more efficient than Rabbit in run-time and communication cost respectively.Includes bibliographical references

    Multiparty computations in varying contexts

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    Recent developments in the automatic transformation of protocols into Secure Multiparty Computation (SMC) interactions, and the selection of appropriate schemes for their implementation have improved usabililty of SMC. Poor performance along with data leakage or errors caused by coding mistakes and complexity had hindered SMC usability. Previous practice involved integrating the SMC code into the application being designed, and this tight integration meant the code was not reusable without modification. The progress that has been made to date towards the selection of different schemes focuses solely on the two-party paradigm in a static set-up, and does not consider changing contexts. Contexts, for secure multiparty computation, include the number of participants, link latency, trust and security requirements such as broadcast, dishonest majority etc. Variable Interpretation is a concept we propose whereby specific domain constructs, such as multiparty computation descriptions, are explicitly removed from the application code and expressed in SMC domain representation. This mirrors current practice in presenting a language or API to hide SMC complexity, but extends it by allowing the interpretation of the SMC to be adapted to the context. It also decouples SMC from human co-ordination by introducing a rule-based dynamic negotiation of protocols. Experiments were carried out to validate the method, running a multiparty computation on a variable interpreter for SMC using different protocols in different contexts

    Applications of Secure Multiparty Computation

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    We generate and gather a lot of data about ourselves and others, some of it highly confidential. The collection, storage and use of this data is strictly regulated by laws, but restricting the use of data often limits the benefits which could be obtained from its analysis. Secure multi-party computation (SMC), a cryptographic technology, makes it possible to execute specific programs on confidential data while ensuring that no other sensitive information from the data is leaked. SMC has been the subject of academic study for more than 30 years, but first attempts to use it for actual computations in the early 2000s – although theoretically efficient – were initially not practicable. However, improvements in the situation have made possible the secure solving of even relatively large computational tasks. This book describes how many different computational tasks can be solved securely, yet efficiently. It describes how protocols can be combined to larger applications, and how the security-efficiency trade-offs of different components of an SMC application should be chosen. Many of the results described in this book were achieved as part of the project Usable and Efficient Secure Multi-party Computation (UaESMC), which was funded by the European Commission. The book will be of interest to all those whose work involves the secure analysis of confidential data

    Information-Theoretic Secure Outsourced Computation in Distributed Systems

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    Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

    Tõhus peit- ja aktiivse ründaja vastu kaitstud turvaline ühisarvutus

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    Turvaline ühisarvutus on tänapäevase krüptograafia üks tähtsamaid kasutusviise, mis koondab elegantsed matemaatilised lahendused praktiliste rakenduste ehitamiseks, võimaldades mitmel erineval andmeomanikul sooritada oma andmetega suvalisi ühiseid arvutusi, ilma neid andmeid üksteisele avaldamata. Passiivse ründaja vastu turvalised protokollid eeldavad, et kõik osapooled käituvad ausalt. Aktiivse ründaja vastu turvalised protokollid ei lekita privaatseid andmeid sõltumata ründaja käitumisest. Käesolevas töös esitatakse üldine meetod, mis teisendab passiivse ründaja vastu turvalised ühisarvutusprotokollid turvaliseks aktiivse ründaja vastu. Meetod on optimeeritud kolme osapoolega arvutusteks üle algebraliste ringide; praktikas on see väga efektiivne mudel, mis teeb reaalse maailma rakendused teostatavateks. Meetod lisab esialgsele arvutusprotokollile täitmisjärgse verifitseerimisfaasi, mis muudab valesti käitunud osapooltel vahelejäämise vältimise tõenäosuse kaduvväikseks, säilitades esialgse protokolli turvagarantiid. Lisaks uurib käesolev töö rünnete uut eesmärki, mis seisneb mingi ausa osapoole vaate manipuleerimises sellisel viisil, et ta saaks midagi teada teise ausa osapoole privaatsete andmete kohta. Ründaja ise ei tarvitse seda infot üldse teada saada. Sellised ründed on olulised, sest need kohustavad ausat osapoolt tühjendama oma süsteemi teiste osapoolte andmetest, kuid see ülesanne võib olla päris mittetriviaalne. Eelnevalt pakutud verifitseerimismehhanisme täiendatakse nii, et privaatsed andmed oleksid kaitstud ka ausate osapoolte eest. Paljud ühisarvutusplatvormid on varustatud programmeerimiskeelega, mis võimaldab kirjutada privaatsust säilitavaid rakendusi ilma allolevale krüptograafiale mõtlemata. Juhul, kui programm sisaldab tingimuslauseid, kus arvutusharu valik sõltub privaatsetest andmetest, ei tohi ükski osapool haru valikust midagi teada, nii et üldjuhul peavad osapooled täitma kõik harud. Harude suure arvu kor-ral võib arvutuslik lisakulu olla ülisuur, sest enamik vahetulemustest visatakse ära. Käesolevas töös pakutakse selliseid lisakulusid vähendavat optimeerimist.Secure multiparty computation is one of the most important employments of modern cryptography, bringing together elegant mathematical solutions to build up useful practical applications. It allows several distinct data owners to perform arbitrary collaborative computation on their private data without leaking any information to each other. Passively secure protocols assume that all parties follow the protocol rules. Actively secure protocols do not leak private data regardless of the attacker’s behaviour. This thesis presents a generic method for turning passively secure multiparty protocols to actively secure ones. The method is optimized for three party computation over algebraic rings, which has proven to be quite an efficient model, making large real-world applications feasible. Our method adds to the protocol a post-execution verification phase that allows a misbehaving party to escape detection only with negligible probability. It preserves the privacy guarantees of the original protocol. In this thesis, we also study a new adversarial goal in multiparty protocols. The goal is to manipulate the view of some honest party in such a way, that this honest party learns the private data of some other honest party. The adversary itself might not learn this data at all. Such attacks are significant because they create a liability to the first honest party to clean its systems from the second honest party’s data, which may be a highly non-trivial task in practice. We check the security of our verification mechanism in this new model, and we propose some minor modifications that ensure data protection also from the honest parties. Many secure multiparty computation platforms come with a programming language that allows the developer to write privacy-preserving applications without thinking of the underlying cryptography. If a program contains conditional statements where the choice of the computational branch depends on private data, then no party should know which branch has been executed, so in general the parties need to execute all of them. If the number of branches is large, the computational overhead may be enormous, as most of the intermediate results are just discarded. In this thesis, we propose an automatic optimization that reduces this overhead

    Towards End-to-End Private Automatic Speaker Recognition

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    The development of privacy-preserving automatic speaker verification systems has been the focus of a number of studies with the intent of allowing users to authenticate themselves without risking the privacy of their voice. However, current privacy-preserving methods assume that the template voice representations (or speaker embeddings) used for authentication are extracted locally by the user. This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable. In this work, we show how speaker embeddings can be extracted while keeping both the speaker's voice and the service provider's model private, using Secure Multiparty Computation. Further, we show that it is possible to obtain reasonable trade-offs between security and computational cost. This work is complementary to those showing how authentication may be performed privately, and thus can be considered as another step towards fully private automatic speaker recognition.Comment: Accepted for publication at Interspeech 202

    Maturity and Performance of Programmable Secure Computation

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    Secure computation research has gained traction internationally in the last five years. In the United States, the DARPA PROCEED program (2011-2015) focused on development of multiple SC paradigms and improving their performance. In the European Union, the PRACTICE program (2013-2016) focuses on its use to secure cloud computing. Both programs have demonstrated exceptional prototypes and performance improvements. In this paper, we collect the results from both programs and other published literature to present the state of the art in what can be achieved with today\u27s secure computing technology. We consider linear secret sharing based computations, garbled circuits and fully homomorphic encryption. We describe theoretical and practical criteria that can be used to characterize secure computation paradigms and provide an overview of common benchmarks such as AES evaluation
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