Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries

We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and other tasks, where the computing nodes is expected to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we try to bridge the gap between theoretical algorithms in the security domain, and a practical Peer-to-Peer deployment. We consider two security models. The first is the semi-honest model where peers correctly follow the protocol, but try to reveal private information. We provide three possible schemes for secure multi-party numerical computation for this model and identify a single light-weight scheme which outperforms the others. Using extensive simulation results over real Internet topologies, we demonstrate that our scheme is scalable to very large networks, with up to millions of nodes. The second model we consider is the malicious peers model, where peers can behave arbitrarily, deliberately trying to affect the results of the computation as well as compromising the privacy of other peers. For this model we provide a fourth scheme to defend the execution of the computation against the malicious peers. The proposed scheme has a higher complexity relative to the semi-honest model. Overall, we provide the Peer-to-Peer network designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA) 200

Spin: An Efficient Secure Computation Framework with GPU Acceleration

Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks. Spin is a GPU-accelerated MPC framework that supports multiple computation parties and a dishonest majority adversarial setup. We propose optimized protocols for non-linear functions that are critical for machine learning, as well as several novel optimizations specific to attention that is the fundamental unit of Transformer models, allowing Spin to perform non-trivial CNNs training and Transformer inference without sacrificing security. At the backend level, Spin leverages GPU, CPU, and RDMA-enabled smart network cards for acceleration. Comprehensive evaluations demonstrate that Spin can be up to $2\times$ faster than the state-of-the-art for deep neural network training. For inference on a Transformer model with 18.9 million parameters, our attention-specific optimizations enable Spin to achieve better efficiency, less communication, and better accuracy

Privacy-preserving Distributed Analytics: Addressing the Privacy-Utility Tradeoff Using Homomorphic Encryption for Peer-to-Peer Analytics

Data is becoming increasingly valuable, but concerns over its security and privacy have limited its utility in analytics. Researchers and practitioners are constantly facing a privacy-utility tradeoff where addressing the former is often at the cost of the data utility and accuracy. In this paper, we draw upon mathematical properties of partially homomorphic encryption, a form of asymmetric key encryption scheme, to transform raw data from multiple sources into secure, yet structure-preserving encrypted data for use in statistical models, without loss of accuracy. We contribute to the literature by: i) proposing a method for secure and privacy-preserving analytics and illustrating its utility by implementing a secure and privacy-preserving version of Maximum Likelihood Estimator, “s-MLE”, and ii) developing a web-based framework for privacy-preserving peer-to-peer analytics with distributed datasets. Our study has widespread applications in sundry industries including healthcare, finance, e-commerce etc., and has multi-faceted implications for academics, businesses, and governments

Input Secrecy & Output Privacy: Efficient Secure Computation of Differential Privacy Mechanisms

Data is the driving force of modern businesses. For example, customer-generated data is collected by companies to improve their products, discover emerging trends, and provide insights to marketers. However, data might contain personal information which allows to identify a person and violate their privacy. Examples of privacy violations are abundant – such as revealing typical whereabout and habits, financial status, or health information, either directly or indirectly by linking the data to other available data sources. To protect personal data and regulate its collection and processing, the general data protection regulation (GDPR) was adopted by all members of the European Union. Anonymization addresses such regulations and alleviates privacy concerns by altering personal data to hinder identification. Differential privacy (DP), a rigorous privacy notion for anonymization mechanisms, is widely deployed in the industry, e.g., by Google, Apple, and Microsoft. Additionally, cryptographic tools, namely, secure multi-party computation (MPC), protect the data during processing. MPC allows distributed parties to jointly compute a function over their data such that only the function output is revealed but none of the input data. MPC and DP provide orthogonal protection guarantees. MPC provides input secrecy, i.e., MPC protects the inputs of a computation via encrypted processing. DP provides output privacy, i.e., DP anonymizes the output of a computation via randomization. In typical deployments of DP the data is randomized locally, i.e., by each client, and aggregated centrally by a server. MPC allows to apply the randomization centrally as well, i.e., only once, which is optimal for accuracy. Overall, MPC and DP augment each other nicely. However, universal MPC is inefficient – requiring large computation and communication overhead – which makes MPC of DP mechanisms challenging for general real-world deployments. In this thesis, we present efficient MPC protocols for distributed parties to collaboratively compute DP statistics with high accuracy. We support general rank-based statistics, e.g., min, max, median, as well as decomposable aggregate functions, where local evaluations can be efficiently combined to global ones, e.g., for convex optimizations. Furthermore, we detect heavy hitters, i.e., most frequently appearing values, over known as well as unknown data domains. We prove the semi-honest security and differential privacy of our protocols. Also, we theoretically analyse and empirically evaluate their accuracy as well as efficiency. Our protocols provide higher accuracy than comparable solutions based on DP alone. Our protocols are efficient, with running times of seconds to minutes evaluated in real-world WANs between Frankfurt and Ohio (100 ms delay, 100 Mbits/s bandwidth), and have modest hardware requirements compared to related work (mainly, 4 CPU cores at 3.3 GHz and 2 GB RAM per party). Additionally, our protocols can be outsourced, i.e., clients can send encrypted inputs to few servers which run the MPC protocol on their behalf

Distributed Cryptographic Protocols

[ES] La confianza es la base de las sociedades modernas. Sin embargo, las relaciones basadas en confianza son difíciles de establecer y pueden ser explotadas fácilmente con resultados devastadores. En esta tesis exploramos el uso de protocolos criptográficos distribuidos para construir sistemas confiables donde la confianza se vea reemplazada por garantías matemáticas y criptográficas. En estos nuevos sistemas dinámicos, incluso si una de las partes se comporta de manera deshonesta, la integridad y resiliencia del sistema están garantizadas, ya que existen mecanismos para superar este tipo de situaciones. Por lo tanto, hay una transición de sistemas basados en la confianza, a esquemas donde esta misma confianza es descentralizada entre un conjunto de individuos o entidades. Cada miembro de este conjunto puede ser auditado, y la verificación universal asegura que todos los usuarios puedan calcular el estado final en cada uno de estos métodos, sin comprometer la privacidad individual de los usuarios. La mayoría de los problemas de colaboración a los que nos enfrentamos como sociedad, pueden reducirse a dos grandes dilemas: el votar una propuesta, o un representante político, ó identificarnos a nosotros mismos como miembros de un colectivo con derecho de acceso a un recurso o servicio. Por ello, esta tesis doctoral se centra en los protocolos criptográficos distribuidos aplicados al voto electrónico y la identificación anónima. Hemos desarrollado tres protocolos para el voto electrónico que complementan y mejoran a los métodos más tradicionales, y además protegen la privacidad de los votantes al mismo tiempo que aseguran la integridad del proceso de voto. En estos sistemas, hemos empleado diferentes mecanismos criptográficos que proveen, bajo diferentes asunciones, de las propiedades de seguridad que todo sistema de voto debe tener. Algunos de estos sistemas son seguros incluso en escenarios pos-cuánticos. También hemos calculado minuciosamente la complejidad temporal de los métodos para demostrar que son eficientes y factibles de ser implementados. Además, hemos implementado algunos de estos sistemas, o partes de ellos, y llevado a cabo una detallada experimentación para demostrar el potencial de nuestras contribuciones. Finalmente, estudiamos en detalle el problema de la identificación y proponemos tres métodos no interactivos y distribuidos que permiten el registro y acceso anónimo. Estos protocolos son especialmente ligeros y agnósticos en su implementación, lo que permite que puedan ser integrados con múltiples propósitos. Hemos formalizado y demostrado la seguridad de nuestros protocolos de identificación, y hemos realizado una implementación completa de ellos para, una vez más, demostrar la factibilidad y eficiencia de las soluciones propuestas. Bajo este marco teórico de identificación, somos capaces de asegurar el recurso custodiado, sin que ello suponga una violación para el anonimato de los usuarios.[CA] La confiança és la base de les societats modernes. No obstant això, les relacions basades en confiança són difícils d’establir i poden ser explotades fàcilment amb resultats devastadors. En aquesta tesi explorem l’ús de protocols criptogràfics distribuïts per a construir sistemes de confiança on la confiança es veja reemplaçada per garanties matemàtiques i criptogràfiques. En aquests nous sistemes dinàmics, fins i tot si una de les parts es comporta de manera deshonesta, la integritat i resiliència del sistema estan garantides, ja que existeixen mecanismes per a superar aquest tipus de situacions. Per tant, hi ha una transició de sistemes basats en la confiança, a esquemes on aquesta acarona confiança és descentralitzada entre un conjunt d’individus o entitats. Cada membre d’aquest conjunt pot ser auditat, i la verificació universal assegura que tots els usuaris puguen calcular l’estat final en cadascun d’aquests mètodes, sense comprometre la privacitat individual dels usuaris. La majoria dels problemes de colůlaboració als quals ens enfrontem com a societat, poden reduir-se a dos grans dilemes: el votar una proposta, o un representant polític, o identificar-nos a nosaltres mateixos com a membres d’un colůlectiu amb dret d’accés a un recurs o servei. Per això, aquesta tesi doctoral se centra en els protocols criptogràfics distribuïts aplicats al vot electrònic i la identificació anònima. Hem desenvolupat tres protocols per al vot electrònic que complementen i milloren als mètodes més tradicionals, i a més protegeixen la privacitat dels votants al mateix temps que asseguren la integritat del procés de vot. En aquests sistemes, hem emprat diferents mecanismes criptogràfics que proveeixen, baix diferents assumpcions, de les propietats de seguretat que tot sistema de vot ha de tindre. Alguns d’aquests sistemes són segurs fins i tot en escenaris post-quàntics. També hem calculat minuciosament la complexitat temporal dels mètodes per a demostrar que són eficients i factibles de ser implementats. A més, hem implementats alguns d’aquests sistemes, o parts d’ells, i dut a terme una detallada experimentació per a demostrar la potencial de les nostres contribucions. Finalment, estudiem detalladament el problema de la identificació i proposem tres mètodes no interactius i distribuïts que permeten el registre i accés anònim. Aquests protocols són especialment lleugers i agnòstics en la seua implementació, la qual cosa permet que puguen ser integrats amb múltiples propòsits. Hem formalitzat i demostrat la seguretat dels nostres protocols d’identificació, i hem realitzat una implementació completa d’ells per a, una vegada més, demostrar la factibilitat i eficiència de les solucions proposades. Sota aquest marc teòric d’identificació, som capaces d’assegurar el recurs custodiat, sense que això supose una violació per a l’anonimat dels usuaris.[EN] Trust is the base of modern societies. However, trust is difficult to achieve and can be exploited easily with devastating results. In this thesis, we explore the use of distributed cryptographic protocols to build reliable systems where trust can be replaced by cryptographic and mathematical guarantees. In these adaptive systems, even if one involved party acts dishonestly, the integrity and robustness of the system can be ensured as there exist mechanisms to overcome these scenarios. Therefore, there is a transition from systems based in trust, to schemes where trust is distributed between decentralized parties. Individual parties can be audited, and universal verifiability ensures that any user can compute the final state of these methods, without compromising individual users’ privacy. Most collaboration problems we face as societies can be reduced to two main dilemmas: voting on a proposal or electing political representatives, or identifying ourselves as valid members of a collective to access a service or resource. Hence, this doctoral thesis focuses on distributed cryptographic protocols for electronic voting and anonymous identification. We have developed three electronic voting schemes that enhance traditional methods, and protect the privacy of electors while ensuring the integrity of the whole election. In these systems, we have employed different cryptographic mechanisms, that fulfill all the desired security properties of an electronic voting scheme, under different assumptions. Some of them are secure even in post-quantum scenarios. We have provided a detailed time-complexity analysis to prove that our proposed methods are efficient and feasible to implement. We also implemented some voting protocols, or parts of them, and carried out meticulous experimentation to show the potential of our contributions. Finally, we study in detail the identification problem and propose three distributed and non-interactive methods for anonymous registration and access. These three protocols are especially lightweight and application agnostic, making them feasible to be integrated with many purposes. We formally analyze and demonstrate the security of our identification protocols, and provide a complete implementation of them to once again show the feasibility and effectiveness of the developed solutions. Using this identification framework, we can ensure the security of the guarded resource, while also preserving the anonymity of the users.Larriba Flor, AM. (2023). Distributed Cryptographic Protocols [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19810

Usalduse vähendamine ja turvalisuse parandamine zk-SNARK-ides ja kinnitusskeemides

Väitekirja elektrooniline versioon ei sisalda publikatsioonezk-SNARK-id on tõhusad ja praktilised mitteinteraktiivsed tõestussüsteemid, mis on konstrueeritud viitestringi mudelis ning tänu kompaktsetele tõestustele ja väga tõhusale verifitseeritavusele on need laialdaselt kasutusele võetud suuremahulistes praktilistes rakendustes. Selles töös uurime zk-SNARK-e kahest vaatenurgast: nende usalduse vähendamine ja turvalisuse tugevdamine. Esimeses suunas uurime kui palju saab vähendada usaldust paaristuspõhiste zk-SNARK-ide puhul ilma nende tõhusust ohverdamata niiviisi, et kasutajad saavad teatud turvataseme ka siis kui seadistusfaas tehti pahatahtlikult või kui avalikustati seadistusfaasi salajane teave. Me pakume välja mõned tõhusad konstruktsioonid, mis suudavad takistada zk-SNARK-i seadistusfaasi ründeid ja mis saavutavad senisest tugevama turvataseme. Näitame ka seda, et sarnased tehnikad võimaldavad leevendada usaldust tagauksega kinnitusskeemides, mis on krüptograafiliste primitiivide veel üks silmapaistev perekond ja mis samuti nõub usaldatud seadistusfaasi. Teises suunas esitame mõned tõhusad konstruktsioonid, mis tagavad parema turvalisuse minimaalsete lisakuludega. Mõned esitatud konstruktsioonidest võimaldavad lihtsustada praegusi TK-turvalisi protokolle, nimelt privaatsust säilitavate nutilepingusüsteemide Hawk ja Gyges konstruktsiooni, ja parandada nende tõhusust. Uusi konstruktsioone saab aga otse kasutada uutes protokollides, mis soovivad kasutada zk-SNARK-e. Osa väljapakutud zk-SNARK-e on implementeeritud teegis Libsnark ja empiirilised tulemused kinnitavad, et usalduse vähendamiseks või suurema turvalisuse saavutamiseks on arvutuslikud lisakulud väikesed.Zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs) are an efficient family of NIZK proof systems that are constructed in the Common Reference String (CRS) model and due to their succinct proofs and very efficient verification, they are widely adopted in large-scale practical applications. In this thesis, we study zk-SNARKs from two perspectives, namely reducing trust and improving security in them. In the first direction, we investigate how much one can mitigate trust in pairing-based zk-SNARKs without sacrificing their efficiency. In such constructions, the parties of protocol will obtain a certain level of security even if the setup phase was done maliciously or the secret information of the setup phase was revealed. As a result of this direction, we present some efficient constructions that can resist against subverting of the setup phase of zk-SNARKs and achieve a certain level of security which is stronger than before. We also show that similar techniques will allow us to mitigate the trust in the trapdoor commitment schemes that are another prominent family of cryptographic primitives that require a trusted setup phase. In the second direction, we present some efficient constructions that achieve more security with minimal overhead. Some of the presented constructions allow to simplify the construction of current UC-secure protocols and improve their efficiency. New constructions can be directly deployed in any novel protocols that aim to use zk-SNARKs. Some of the proposed zk-SNARKs are implemented in Libsnark, the state-of-the-art library for zk-SNARKs, and empirical experiences confirm that the computational cost to mitigate the trust or to achieve more security is practical.https://www.ester.ee/record=b535927

Turvalisel ühisarvutusel põhinev privaatsust säilitav statistiline analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Kaasaegses ühiskonnas luuakse inimese kohta digitaalne kirje kohe pärast tema sündi. Sellest hetkest alates jälgitakse tema käitumist ning kogutakse andmeid erinevate eluvaldkondade kohta. Kui kasutate poes kliendikaarti, käite arsti juures, täidate maksudeklaratsiooni või liigute lihtsalt ringi mobiiltelefoni taskus kandes, koguvad ning salvestavad firmad ja riigiasutused teie tundlikke andmeid. Vahel anname selliseks jälitustegevuseks vabatahtlikult loa, et saada mingit kasu. Näiteks võime saada soodustust, kui kasutame kliendikaarti. Teinekord on meil vaja teha keeruline otsus, kas loobuda võimalusest teha mobiiltelefonikõnesid või lubada enda jälgimine mobiilimastide kaudu edastatava info abil. Riigiasutused haldavad infot meie tervise, hariduse ja sissetulekute kohta, et meid paremini ravida, harida ja meilt makse koguda. Me loodame, et meie andmeid kasutatakse mõistlikult, aga samas eeldame, et meie privaatsus on tagatud. Käesolev töö uurib, kuidas teostada statistilist analüüsi nii, et tagada üksikisiku privaatsus. Selle eesmärgi saavutamiseks kasutame turvalist ühisarvutust. See krüptograafiline meetod lubab analüüsida andmeid nii, et üksikuid väärtuseid ei ole kunagi võimalik näha. Hoolimata sellest, et turvalise ühisarvutuse kasutamine on aeganõudev protsess, näitame, et see on piisavalt kiire ja seda on võimalik kasutada isegi väga suurte andmemahtude puhul. Me oleme teinud võimalikuks populaarseimate statistilise analüüsi meetodite kasutamise turvalise ühisarvutuse kontekstis. Me tutvustame privaatsust säilitavat statistilise analüüsi tööriista Rmind, mis sisaldab kõiki töö käigus loodud funktsioone. Rmind sarnaneb tööriistadele, millega statistikud on harjunud. See lubab neil viia läbi uuringuid ilma, et nad peaksid üksikasjalikult tundma allolevaid krüptograafilisi protokolle. Kasutame dissertatsioonis kirjeldatud meetodeid, et valmistada ette statistiline uuring, mis ühendab kaht Eesti riiklikku andmekogu. Uuringu eesmärk on teada saada, kas Eesti tudengid, kes töötavad ülikooliõpingute ajal, lõpetavad nominaalajaga väiksema tõenäosusega kui nende õpingutele keskenduvad kaaslased.In a modern society, from the moment a person is born, a digital record is created. From there on, the person’s behaviour is constantly tracked and data are collected about the different aspects of his or her life. Whether one is swiping a customer loyalty card in a store, going to the doctor, doing taxes or simply moving around with a mobile phone in one’s pocket, sensitive data are being gathered and stored by governments and companies. Sometimes, we give our permission for this kind of surveillance for some benefit. For instance, we could get a discount using a customer loyalty card. Other times we have a difficult choice – either we cannot make phone calls or our movements are tracked based on cellular data. The government tracks information about our health, education and income to cure us, educate us and collect taxes. We hope that the data are used in a meaningful way, however, we also have an expectation of privacy. This work focuses on how to perform statistical analyses in a way that preserves the privacy of the individual. To achieve this goal, we use secure multi-­‐party computation. This cryptographic technique allows data to be analysed without seeing the individual values. Even though using secure multi-­‐party computation is a time-­‐consuming process, we show that it is feasible even for large-­‐scale databases. We have developed ways for using the most popular statistical analysis methods with secure multi-­‐party computation. We introduce a privacy-­‐preserving statistical analysis tool called Rmind that contains all of our resulting implementations. Rmind is similar to tools that statistical analysts are used to. This allows them to carry out studies on the data without having to know the details of the underlying cryptographic protocols. The methods described in the thesis are used in practice to prepare for running a statistical study on large-­‐scale real-­‐life data to find out whether Estonian students who are working during university studies are less likely to graduate in nominal time