Electronic data has become the standard method of storing information in our modern age. Evolving from paper-based data to electronic data creates opportunities to share information between organizations in record speeds, especially when handling large data sets. However, sharing sensitive information creates requirements for electronic data exchange: privacy requires that the original data will not be revealed to unauthorized parties. In the healthcare sector in particular, there are two important use cases that require exchanging information in a privacy-preserving way. 1. Contract research organizations (CROs) need to verify the eligibility of a participant in a phase 1 clinical trial. One criterion is checking that an individual is not concurrently enrolled in a trial at another CRO. However, privacy laws and the maintenance of a private list of participants for competitive purposes prevent CROs from checking against that criterion. 2. A patient’s medical record is usually distributed amongst several healthcare organizations. To improve healthcare services, it is important to have a patient’s complete medical history: either to help diagnose an illness or to gather statistics for better disease control. However, patient medical files need to be confidential. Two healthcare organizations cannot link their large patient databases by disclosing identity revealing details (e.g., names or health card numbers). This thesis presents the development and evaluation of protocols capable of querying and linking datasets in a privacy-preserving manner: TRACK for checking concurrent enrolment in phase 1 clinical trials, and SHARE for linking two large datasets in terms of millions of (patient medical) records. These protocols are better than existing approaches in terms of the privacy protection level they offer (e.g., against dictionary and frequency attacks), of the reliance on trusted third parties, and of performance when performing blocking. These protocols were extensively validated in simulated scenarios similar to their real-world counterparts. The thesis presents novel identity representation schemes that offer strong privacy measures while being efficient for very large databases. These schemes may be used by other researchers to represent identity in different use cases. CROs may implement the protocols (and especially TRACK) in systems to check if an individual exists in another CRO’s dataset without revealing the identity of that individual. Two healthcare organizations may use a system based on this research (and especially the SHARE protocol) to discover their common patients while protecting the identities of the other patients.

Security and privacy are of paramount importance in the modern information age. Secure multi-party computation and private information retrieval are canonical and representative problems in cryptography that capture the key challenges in understanding the fundamentals of security and privacy. In this dissertation, we use information theoretic tools to tackle these two classical cryptographic primitives. In the first part, we consider the secure multi-party computation problem, where multiple users, each holding an independent message, wish to compute a function on the messages without revealing any additional information. We present an efficient protocol in terms of randomness cost to securely compute a vector linear function. In the second part, we discuss the symmetric private information retrieval problem, where a user wishes to retrieve one message from a number of replicated databases while keeping the desired message index a secret from each individual database. Further, the user learns nothing about the other messages. We present an optimal protocol that achieves the minimum upload cost for symmetric private information retrieval, i.e., the queries sent from the user to the databases have the minimum number of bits.

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's 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.

資訊安全的研究裡，運用安全多方計算的方法，可使得多方在不洩漏各自私有資訊的條件下完成某種函式的計算。其中一種做法是利用scalar product來當作計算的基礎演算邏輯單元，並進而建構其他更複雜的安全多方計算。 根據目前現有的安全多方運算協定，可再加以定義出一些基本的運算規則，像是一般的程式語言中常用到的變數型態，如整數、浮點數、布林值，我們可定義出安全的秘密資料形態來，並且要能達到算數計算就必須擁有數值處理的能力，如基本的四則運算等，所以提供了相關聯的安全計算協定。根據安全多方計算的運算平台，可具有處理算術計算的能力，使得可處理一般安全計算的問題。 我們並提供一個script轉譯工具，使得使用者可自行撰寫自己的安全多方計算程式，並可利用此工具來自動將使用者寫的程式碼轉成安全多方運算平台可接受的程式碼，如此一來，解決安全多方計算的問題將會變得更為容易。 / Protocols for secure multi-party computation (SMC) allow participants to share a computation while each party learns only what can be inferred from their own inputs and the output of the computation. This thesis concerns the implementation SMC using of a set of information theoretically secure protocols based on scalar product protocol. This main characteristic of this approach is taking the scalar product computation as the basic building, and then use it to construct more complex computation protocols. We developed an SMC implementation framework for both integers and floating numbers which comprises a set of arithmetic operations that manipulate secret values among involved parties using the scalar product protocol as the basis. Such a library of arithmetic operations is call building blocks. Besides, to ease the writing of more complex user-defined protocols, we developed a simple scripting language and a translation tool that converts user script code to SMC code, which is code composed of the building blocks we developed.

Oblivious Transfer (OT) is one of the most fundamental cryptographic primitives with wide-spread application in general secure multi-party computation (MPC) as well as in a number of tailored and special-purpose problems of interest such as private set intersection (PSI), private information retrieval (PIR), contract signing to name a few. Often the instantiations of OT require prohibitive communication and computation complexity. OT extension protocols are introduced to compute a very large number of OTs referred as extended OTs at the cost of a small number of OTs referred as seed OTs. We present a fast OT extension protocol for small secrets in active setting. Our protocol when used to produce 1-out-of-n OTs outperforms all the known actively secure OT extensions. Our protocol is built on the semi-honest secure extension protocol of Kolesnikov and Kumaresan of CRYPTO'13 (referred as KK13 protocol henceforth) which is the best known OT extension for short secrets. At the heart of our protocol lies an efficient consistency checking mechanism that relies on the linearity of Walsh-Hadamard (WH) codes. Asymptotically, our protocol adds a communication overhead of O( log ) bits over KK13 protocol irrespective of the number of extended OTs, where and refer to computational and statistical security parameter respectively. Concretely, our protocol when used to generate a large enough number of OTs adds only 0:011-0:028% communication overhead and 4-6% runtime overhead both in LAN and WAN over KK13 extension. The runtime overheads drop below 2% when in addition the number of inputs of the sender in the extended OTs is large enough. As an application of our proposed extension protocol, we show that it can be used to obtain the most efficient PSI protocol secure against a malicious receiver and a semi-honest sender.

My research investigates three questions: How do we customize protocols and implementations to account for the unique requirement of each setting and its target community, what are necessary steps that we can take to transition secure computation tools into practice, and how can we promote their adoption for users at large? In this dissertation I present several of my works that address these three questions with a particular focus on one of them. First my work on "Hecate: Abuse Reporting in Secure Messengers with Sealed Sender" designs a customized protocol to protect people from abuse and surveillance in online end to end encrypted messaging. Our key insight is to add pre-processing to asymmetric message franking, where the moderating entity can generate batches of tokens per user during off-peak hours that can later be deposited when reporting abuse. This thesis then demonstrates that by carefully tailoring our cryptographic protocols for real world use cases, we can achieve orders of magnitude improvements over prior works with minimal assumptions over the resources available to people. Second, my work on "Batched Differentially Private Information Retrieval" contributes a novel Private Information Retrieval (PIR) protocol called DP-PIR that is designed to provide high throughput at high query rates. It does so by pushing all public key operations into an offline stage, batching queries from multiple clients via techniques similar to mixnets, and maintain differential privacy guarantees over the access patterns of the database. Finally, I provide three case studies showing that we cannot hope to further the adoption of cryptographic tools in practice without collaborating with the very people we are trying to protect. I discuss a pilot deployment of secure multi-party computation (MPC) that I have done with the Department of Education, deployments of MPC I have done for the Boston Women’s Workforce Council and the Greater Boston Chamber of Commerce, and ongoing work in developing tool chain support for MPC via an automated resource estimation tool called Carousels.

安全多方計算是資訊安全研究裡的一個重要主題，其概念為多方在不洩漏各自私有資訊下能一起完成某種函式的計算。在安全多方計算研究領域裡，有一種作法是以scalar product來當作計算的基礎演算邏輯單元，重而建構其他更複雜的安全多方計算。本論文首先針對scalar product發展一套平行性實作架構，藉此我們再實作出多個不同演算法之comparison計算，其中包含了循序演算法以及平行演算法。我們透過實驗來找出適當的平行計算基礎架構與影響執行時間效能的主要因子，並以執行時間效能上的分析來推導相關時間公式。由上述實證研究我們對於不同演算法之comparison計算來作執行時間效能的預測，從實驗結果可以得知我們推導出來之時間公式極為準確，希望能給予使用者在執行comparison計算有所考量，使其在不同執行環境執行comparison計算能有最佳的執行時間效能。 / Loosely speaking, secure multi-party computation (SMC) involves computing functions with inputs from two or more parties in a distributed network while ensuring that no additional information, other than what can be inferred from each participant’s input and output, is revealed to parties not privy to that information. This thesis concerns the parallel implementation of SMC using a scalar-product (SP) based approach. In this approach, SP is considered as the basic building block for constructing more complex SMC. My thesis first develops a concurrent architecture for implementing two-party scalar product computation. Then it implements several algorithms of secure comparison. Finally, a series of experiments are conducted to collect performance statistics for building time functions that can predict the execution time of comparison computation based on that of the scalar product and other parameters, such as CPU core numbers. From the experimental results, we find that these time functions are very accurate. Hence we argue that these time functions can assist users to obtain the better runtime performance for comparison protocols under their specific execution environments.

Dans un monde de plus en plus connecté, la question de la confiance dans les sys-tèmes d’information qui nous entourent devient primordiale, et amène naturellement à des interrogations quant à leur sécurité. Les enjeux de cette dernière concernent autant la confidentialité des données individuelles que la protection des architectures critiques, notamment déployées dans le domaine de l’énergie et du transport. Dans cette thèse, nous abordons trois problématiques liées aux architectures de sécurité des systèmes d’information. Tout d’abord, nous proposons une architecture pour un module de rupture protocolaire, fournissant une protection face aux attaques utilisant le réseau comme vecteur. Grâce à l’isolation et le filtrage des échanges qu’il réalise, nous montrons que ce nouvel équipement est particulièrement adapté à la sécurisation des systèmes de contrôle-commandes. Nous abordons ensuite le thème de la sécurité des utilisateurs finaux ou objets connectés, par la définition d’une Infrastructure de Gestion de Clefs (IGC) centrée sur ces derniers, dénommée LocalPKI. Elle repose sur l’utilisation de certificats auto-signés, et son objectif est d’allier la simplicité des IGC pair-à-pair avec la sécurité des IGC hiérarchiques.Enfin, nous nous intéressons à l’amélioration du mécanisme des ancres de confiance pour les autorités de certification, utilisé par exemple dans PKIX et LocalPKI. A cet égard, nous commençons par définir des protocoles multi-parties permettant de calculer des produits scalaires et matriciels, préservant la confidentialité des données. Nous montrons finalement comment les appliquer dans le cadre de l’agrégation de confiance, et par conséquent à la réputation des autorités de certification / In a increasingly connected world, trust in information systems is essential. Thus, many questions about their security arise. Topics of these questions include individual data confidentiality as well as protection of Industrial Critical Systems(ICS). For instance, ICS are deployed in sectors including energy or transportation where security is of high importance. In this thesis, we address three problems related to the security architecture of information systems. We first propose an architecture for a protocol splitting device. This provides protection against networkattacks by isolating and filtering data exchanges. We show that this new security equipment is well suited for ICS. Then, we focus on end-user security. We define a user-centric Public Key Infrastructure (PKI) called LocalPKI. By using self-signed certificates, this infrastructure combines the user-friendliness of PGP-based PKI and the security of hierarchical PKI. Finally, we improve the trust anchormechanism. It is employed by Certification Authorities (CA) and especially used in PKIX or LocalPKI. In that respect, we first define multi-party protocols to securely compute dot and matrix products. Then, we explain how to apply them on trust aggregations and thus on the reputation of certification authorities.

Une large quantité de données personnelles sont partagées en temps réel par des utilisateurs en ligne, utilisant de plus en plus des terminaux mobiles avec connexion sans-fil. L'industrie s'efforce d'accumuler et d'analyser ces données pour fournir de nouveaux services ou des améliorations. La recherche fournit un effort équivalent pour permettre de traiter ces données de façon sécurisée et protectrice de la vie privée. Les problèmes de performance des communications temps réels sur terminaux mobiles sur un canal sans-fil sont aussi étudiés. Les codes à effacement sont un moyen courant d'améliorer ces performances. Le secret sharing est un mécanisme permettant de partager des données privées, ne les révélant qu'à un groupe d'utilisateur choisi. Dans cette thèse, nous lions théoriquement les secret sharing schemes et les codes à effacement, pour fournir une source plus riche de solutions aux deux problèmes. Notre objectif est de fournir des solutions ayant le niveau de sécurité souhaité, tout en restant efficace et implémentable. Les contributions de cette thèse sont les suivantes. Nous évaluons l'applicabilité d'une nouvelle classe de codes à effacements à Maximum Distance Séparable (MDS) pour transférer du contenu temps réel à des terminaux mobiles, et nous démontrons que le code systématique réduit grandement la complexité d'exécution et la taille nécessaire des tampons en comparaison du code non systématique, faisant de lui un bon candidat pour une application mobile. Nous proposons un nouveau Layered secret sharing scheme pour le partage en temps réel de données sur des réseaux sociaux (OSNs pour Online Social Network). Le procédé permet de partager automatiquement un profile dans un groupe défini dans un OSN, en utilisant un multi-secret sharing scheme formé de multiples couches. Le procédé ne dépend nullement d'un tiers de confiance. Comparé à un partage simple de chaque attributs (pouvant être un texte, une image ou une vidéo), le procédé ne divulgue aucune information à propos de ce qui est partagé, pas même le nombre de ceux-ci, et il induit une augmentation relativement faible du temps de calcul et des données à envoyer. Finalement, nous étudions les liens entre les codes MDS et les secret sharing schemes, ayant pour motivation l'inefficacité du très populaire Shamir secret sharing scheme. Nous établissons les liens théoriques entre les deux domaines et nous proposons une nouvelle construction de strong ramp schemes à partir de codes MDS. Ceci permet d'utiliser les codes MDS existants et efficaces pour des applications de partage de secret et de calculs distribués et sécurisés. Nous évaluons et montrons une réduction significative de temps de calcul et du coût de communication en utilisant un strong ramp scheme, en comparaison avec le procédé de Shamir. / Data from both individuals and companies is increasingly aggregated and analysed to provide new and improved services. There is a corresponding research effort to enable processing of such data in a secure and privacy preserving way, in line with the increasing public concerns and more stringent regulatory requirements for the protection of such data. Secure Multi-Party Computation (MPC) and secret sharing are mechanisms that can enable both secure distribution and computations on private data. In this thesis, we address the inefficiencies of these mechanisms by utilising results from a theoretically related rich area, erasure codes. We derive links between erasure codes and secret sharing, and use Maximum Distance Separable (MDS) codes as a basis to provide real-time applications relying on private user's data, revealing this data only to the selected group (which can be empty). The thesis has three contributions. A new class of erasure code called on-the-fly coding, have been introduced for their improvements in terms of recovery delay and achievable capacity. However little is known about the complexity of the systematic and non-systematic variants of this code, notably for live multicast transmission of multimedia content which is their ideal use case. The evaluation of both variants demonstrate that the systematic code outperforms the non-systematic one in regard to both the buffer sizes and the computation complexity. Then, we propose a new Layered secret sharing scheme and its application to Online Social Network (OSN). In current OSN, access to the user's profile information is managed by the service provider based on a limited set of rules. The proposed scheme enables automated profile sharing in OSN's groups with fine grained privacy control, via a multi-secret sharing scheme comprising of layered shares, without relying on a trusted third party. We evaluate the security of the scheme and the resulting profile's level of protection in an OSN scenario. Finally, after showing that erasure codes are efficient for real-time applications and that the security offered by secret sharing schemes can be applied to real-case applications, we derive the theoretical links between MDS codes and secret sharing to enable the implementation of efficient secret sharing scheme built from MDS codes. To illustrate this efficiency, we implement two of these schemes and evaluate their benefits in regard to computation and communication costs in an MPC application.

Nous présentons une nouvelle approche de préservation de la vie privée pour améliorer l’équité des éléments dans les systèmes de classement. Nous utilisons des techniques de post-traitement dans un environnement de recommandation multipartite afin d’équilibrer l’équité et la protection de la vie privée pour les producteurs et les consommateurs. Notre méthode utilise des serveurs de calcul multipartite sécurisés (MPC) et une confidentialité différentielle (DP) pour maintenir la confidentialité des utilisateurs tout en atténuant l’injustice des éléments sans compromettre l’utilité. Les utilisateurs soumettent leurs données sous forme de partages secrets aux serveurs MPC, et tous les calculs sur ces données restent cryptés. Nous évaluons notre approche à l’aide d’ensembles de données du monde réel, tels qu’Amazon Digital Music, Book Crossing et MovieLens-1M, et analysons les compromis entre confidentialité, équité et utilité. Notre travail encourage une exploration plus approfondie de l’intersection de la confidentialité et de l’équité dans les systèmes de recommandation, jetant les bases de l’intégration d’autres techniques d’amélioration de la confidentialité afin d’optimiser l’exécution et l’évolutivité pour les applications du monde réel. Nous envisageons notre approche comme un tremplin vers des solutions de bout en bout préservant la confidentialité et promouvant l’équité dans des environnements de recommandation multipartites. / We present a novel privacy-preserving approach to enhance item fairness in ranking systems. We employ post-processing techniques in a multi-stakeholder recommendation environment in order to balance fairness and privacy protection for both producers and consumers. Our method utilizes secure multi-party computation (MPC) servers and differential privacy (DP) to maintain user privacy while mitigating item unfairness without compromising utility. Users submit their data as secret shares to MPC servers, and all calculations on this data remain encrypted. We evaluate our approach using real-world datasets, such as Amazon Digital Music, Book Crossing, and MovieLens-1M, and analyze the trade-offs between privacy, fairness, and utility. Our work encourages further exploration of the intersection of privacy and fairness in recommender systems, laying the groundwork for integrating other privacy-enhancing techniques to optimize runtime and scalability for real-world applications. We envision our approach as a stepping stone towards end-to-end privacy-preserving and fairness-promoting solutions in multi-stakeholder recommendation environments.