Cloud computing has brought great benefits in cost and flexibility for provisioning services. The greatest challenge of cloud computing remains however the question of security. The current standard tools in access control mechanisms and cryptography can only partly solve the security challenges of cloud infrastructures. In the recent years of research in security and cryptography, novel mechanisms, protocols and algorithms have emerged that offer new ways to create secure services atop cloud infrastructures. This report provides introductions to a selection of security mechanisms that were part of the "Cloud Security Mechanisms" seminar in summer term 2013 at HPI. / Cloud Computing hat deutliche Kostenersparnisse und verbesserte Flexibilität bei der Bereitstellung von Computer-Diensten ermöglicht. Allerdings bleiben Sicherheitsbedenken die größte Herausforderung bei der Nutzung von Cloud-Diensten. Die etablierten Mechanismen für Zugriffskontrolle und Verschlüsselungstechnik können die Herausforderungen und Probleme der Sicherheit von Cloud-Infrastrukturen nur teilweise lösen. In den letzten Jahren hat die Forschung jedoch neue Mechanismen, Protokolle und Algorithmen hervorgebracht, welche neue Möglichkeiten eröffnen die Sicherheit von Cloud-Anwendungen zu erhöhen. Dieser technische Bericht bietet Einführungen zu einigen dieser Mechanismen, welche im Seminar "Cloud Security Mechanisms" im Sommersemester 2013 am HPI behandelt wurden.

Une des avancées les plus notables de ces dernières années en cryptographie est sans contredit l’introduction du premier schéma de chiffrement complètement homomorphe par Craig Gentry. Ce type de système permet de réaliser des calculs arbitraires sur des données chiffrées, sans les déchiffrer. Cette particularité permet de répondre aux exigences de sécurité et de protection des données, par exemple dans le cadre en plein développement de l'informatique en nuage et de l'internet des objets. Les algorithmes mis en œuvre sont actuellement très coûteux en temps de calcul, et généralement implantés sous forme logicielle. Les travaux de cette thèse portent sur l’accélération matérielle de schémas de chiffrement homomorphes. Une étude des primitives utilisées par ces schémas et la possibilité de leur implantation matérielle est présentée. Ensuite, une nouvelle approche permettant l’implantation des deux fonctions les plus coûteuses est proposée. Notre approche exploite les capacités offertes par la synthèse de haut niveau. Elle a la particularité d’être très flexible et générique et permet de traiter des opérandes de tailles arbitraires très grandes. Cette particularité lui permet de viser un large domaine d’applications et lui autorise d’appliquer des optimisations telles que le batching. Les performances de notre architecture de type co-conception ont été évaluées sur l’un des cryptosystèmes homomorphes les plus récents et les plus efficaces. Notre approche peut être adaptée aux autres schémas homomorphes ou plus généralement dans le cadre de la cryptographie à base de réseaux. / One of the most significant advances in cryptography in recent years is certainly the introduction of the first fully homomorphic encryption scheme by Craig Gentry. This type of cryptosystem allows performing arbitrarily complex computations on encrypted data, without decrypting it. This particularity allows meeting the requirements of security and data protection, for example in the context of the rapid development of cloud computing and the internet of things. The algorithms implemented are currently very time-consuming, and most of them are implemented in software. This thesis deals with the hardware acceleration of homomorphic encryption schemes. A study of the primitives used by these schemes and the possibility of their hardware implementation is presented. Then, a new approach allowing the implementation of the two most expensive functions is proposed. Our approach exploits the high-level synthesis. It has the particularity of being very flexible and generic and makes possible to process operands of arbitrary large sizes. This feature allows it to target a wide range of applications and to apply optimizations such as batching. The performance of our co-design was evaluated on one of the most recent and efficient homomorphic cryptosystems. It can be adapted to other homomorphic schemes or, more generally, in the context of lattice-based cryptography.

Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users’ fine-grained metering data measured by users’ Smart Meters (SMs). The SEV charging application allows EVs’ charging parameters to be changed depending on the grid’s state in return for incentives for the EV owners. However, both applications impose risks on users’ privacy. Entities having access to users’ fine-grained metering data may use such data to infer individual users’ personal habits. In addition, users’ private information such as users’/EVs’ identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users’ whereabouts, thus breach their privacy. This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner. First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users’ data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases. Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users’ fine-grained operational metering data while preserving users’ privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users’ privacy with minimum performance penalty, users’ metering data are classified and aggregated accordingly by their respective local gateways based on the users’ locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work. Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user’s accountable data, (ii) this register is updated by design at a low frequency, (iii) the user’s supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads. Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charging at home location. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user’s contracted supplier) which is anonymously signed by the user’s private key. This protocol protects the user’s identity privacy from other suppliers as well as the user’s privacy of location from its own supplier. Further, it allows the user’s contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multi-user EV charging is supported and different legitimate EV users (e.g., family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the same EV. On an application level, REVCBAMU supports fair user billing, i.e., each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers. The protocol has been analysed in terms of security and computational overheads.

<div> <div> <div> <p>Cloud computing offers a cost-efficient data analytics platform. This is enabled by constant innovations in tools and technologies for analyzing large volumes of data through distributed batch processing systems and real-time data through distributed stream processing systems. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. To address this stalemate, both software-based and hardware-based solutions have been proposed yet all have substantial limitations in terms of efficiency, expressiveness, and security. In this thesis, we present solutions that enable practical and expressive confidentiality- preserving batch and stream-based analytics. We achieve this by performing computations over encrypted data using Partially Homomorphic Encryption (PHE) and Property-Preserving Encryption (PPE) in novel ways, and by utilizing remote or Trusted Execution Environment (TEE) based trusted services where needed.</p><p><br></p><p>We introduce a set of extensions and optimizations to PHE and PPE schemes and propose the novel abstraction of Secure Data Types (SDTs) which enables the application of PHE and PPE schemes in ways that improve performance and security. These abstractions are leveraged to enable a set of compilation techniques making data analytics over encrypted data more practical. When PHE alone is not expressive enough to perform analytics over encrypted data, we use a novel planner engine to decide the most efficient way of utilizing client-side completion, remote re-encryption, or trusted hardware re-encryption based on Intel Software Guard eXtensions (SGX) to overcome the limitations of PHE. We also introduce two novel symmetric PHE schemes that allow arithmetic operations over encrypted data. Being symmetric, our schemes are more efficient than the state-of-the-art asymmetric PHE schemes without compromising the level of security or the range of homomorphic operations they support. We apply the aforementioned techniques in the context of batch data analytics and demonstrate the improvements over previous systems. Finally, we present techniques designed to enable the use of PHE and PPE in resource-constrained Internet of Things (IoT) devices and demonstrate the practicality of stream processing over encrypted data.</p></div></div></div><div><div><div> </div> </div> </div>

Un nombre important et en constante augmentation de systèmes numériques nous entoure. Tablettes, smartphones et objets connectés ne sont que quelques exemples apparents de ces technologies omniprésentes, dont la majeure partie est enfouie, invisible à l'utilisateur. Les microprocesseurs, au cœur de ces systèmes, sont soumis à de fortes contraintes en ressources, sûreté de fonctionnement et se doivent, plus que jamais, de proposer une sécurité renforcée. La tâche est d'autant plus complexe qu'un tel système, par sa proximité avec l'utilisateur, offre une large surface d'attaque.Cette thèse, se concentre sur une propriété essentielle attendue pour un tel système, la confidentialité, le maintien du secret du programme et des données qu'il manipule. En effet, l'analyse du programme, des instructions qui le compose, est une étape essentielle dans la conception d'une attaque. D'autre part, un programme est amené à manipuler des données sensibles (clés cryptographiques, mots de passes, ...), qui doivent rester secrètes pour ne pas compromettre la sécurité du système.Cette thèse, se concentre sur une propriété essentielle attendue pour un tel système, la confidentialité, le maintien du secret du programme et des données qu'il manipule. Une première contribution de ces travaux est une méthode de chiffrement d'un code, basée sur le graphe de flot de contrôle, rendant possible l'utilisation d'algorithmes de chiffrement par flots, légers et efficaces. Protéger les accès mémoires aux données d'un programme s'avère plus complexe. Dans cette optique, nous proposons l'utilisation d'un chiffrement homomorphe pour chiffrer les données stockées en mémoire et les maintenir sous forme chiffrée lors de l'exécution des instructions. Enfin, nous présenterons l'intégration de ces propositions dans une architecture de processeur et les résultats d'évaluation sur logique programmable (FPGA) avec plusieurs programmes d'exemples. / Embedded processors are today ubiquitous, dozen of them compose and orchestrate every technology surrounding us, from tablets to smartphones and a large amount of invisible ones. At the core of these systems, processors gather data, process them and interact with the outside world. As such, they are excepted to meet very strict safety and security requirements. From a security perspective, the task is even more difficult considering the user has a physical access to the device, allowing a wide range of specifically tailored attacks.Confidentiality, in terms of both software code and data is one of the fundamental properties expected for such systems. The first contribution of this work is a software encryption method based on the control flow graph of the program. This enables the use of stream ciphers to provide lightweight and efficient encryption, suitable for constrained processors. The second contribution is a data encryption mechanism based on homomorphic encryption. With this scheme, sensible data remain encrypted not only in memory, but also during computations. Then, the integration and evaluation of these solutions on Field Programmable Gate Array (FPGA) with some example programs will be discussed.

Le chiffrement homomorphe est une branche de la cryptologie, dans laquelle les schémas de chiffrement offrent la possibilité de faire des calculs sur les messages chiffrés, sans besoin de les déchiffrer. L’intérêt pratique de ces schémas est dû à l’énorme quantité d'applications pour lesquels ils peuvent être utilisés. En sont un exemple le vote électronique, les calculs sur des données sensibles, comme des données médicales ou financières, le cloud computing, etc..Le premier schéma de chiffrement (complètement) homomorphe n'a été proposé qu'en 2009 par Gentry. Il a introduit une technique appelée bootstrapping, utilisée pour réduire le bruit des chiffrés : en effet, dans tous les schémas de chiffrement homomorphe proposés, les chiffrés contiennent une petite quantité de bruit, nécessaire pour des raisons de sécurité. Quand on fait des calculs sur les chiffrés bruités, le bruit augmente et, après avoir évalué un certain nombre d’opérations, ce bruit devient trop grand et, s'il n'est pas contrôlé, risque de compromettre le résultat des calculs.Le bootstrapping est du coup fondamental pour la construction des schémas de chiffrement homomorphes, mais est une technique très coûteuse, qu'il s'agisse de la mémoire nécessaire ou du temps de calcul. Les travaux qui on suivi la publication de Gentry ont eu comme objectif celui de proposer de nouveaux schémas et d’améliorer le bootstrapping pour rendre le chiffrement homomorphe faisable en pratique. L’une des constructions les plus célèbres est GSW, proposé par Gentry, Sahai et Waters en 2013. La sécurité du schéma GSW se fonde sur le problème LWE (learning with errors), considéré comme difficile en pratique. Le bootstrapping le plus rapide, exécuté sur un schéma de type GSW, a été proposé en 2015 par Ducas et Micciancio. Dans cette thèse on propose une nouvelle variante du schéma de chiffrement homomorphe de Ducas et Micciancio, appelée TFHE.Le schéma TFHE améliore les résultats précédents, en proposant un bootstrapping plus rapide (de l'ordre de quelques millisecondes) et des clés de bootstrapping plus petites, pour un même niveau de sécurité. TFHE utilise des chiffrés de type TLWE et TGSW (scalaire et ring) : l’accélération du bootstrapping est principalement due à l’utilisation d’un produit externe entre TLWE et TGSW, contrairement au produit externe GSW utilisé dans la majorité des constructions précédentes.Deux types de bootstrapping sont présentés. Le premier, appelé gate bootstrapping, est exécuté après l’évaluation homomorphique d’une porte logique (binaire ou Mux) ; le deuxième, appelé circuit bootstrapping, peut être exécuté après l’évaluation d’un nombre d'opérations homomorphiques plus grand, pour rafraîchir le résultat ou pour le rendre compatible avec la suite des calculs.Dans cette thèse on propose aussi de nouvelles techniques pour accélérer l’évaluation des calculs homomorphiques, sans bootstrapping, et des techniques de packing des données. En particulier, on présente un packing, appelé vertical packing, qui peut être utilisé pour évaluer efficacement des look-up table, on propose une évaluation via automates déterministes pondérés, et on présente un compteur homomorphe appelé TBSR qui peut être utilisé pour évaluer des fonctions arithmétiques.Pendant les travaux de thèse, le schéma TFHE a été implémenté et il est disponible en open source.La thèse contient aussi des travaux annexes. Le premier travail concerne l’étude d’un premier modèle théorique de vote électronique post-quantique basé sur le chiffrement homomorphe, le deuxième analyse la sécurité des familles de chiffrement homomorphe dans le cas d'une utilisation pratique sur le cloud, et le troisième ouvre sur une solution différente pour le calcul sécurisé, le calcul multi-partite. / Fully homomorphic encryption is a new branch of cryptology, allowing to perform computations on encrypted data, without having to decrypt them. The main interest of homomorphic encryption schemes is the large number of practical applications for which they can be used. Examples are given by electronic voting, computations on sensitive data, such as medical or financial data, cloud computing, etc..The first fully homomorphic encryption scheme has been proposed in 2009 by Gentry. He introduced a new technique, called bootstrapping, used to reduce the noise in ciphertexts: in fact, in all the proposed homomorphic encryption schemes, the ciphertexts contain a small amount of noise, which is necessary for security reasons. If we perform computations on noisy ciphertexts, the noise increases and, after a certain number of operations, the noise becomes to large and it could compromise the correctness of the final result, if not controlled.Bootstrapping is then fundamental to construct fully homomorphic encryption schemes, but it is very costly in terms of both memory and time consuming.After Gentry’s breakthrough, the presented schemes had the goal to propose new constructions and to improve bootstrapping, in order to make homomorphic encryption practical. One of the most known schemes is GSW, proposed by Gentry, Sahai et Waters in 2013. The security of GSW is based on the LWE (learning with errors) problem, which is considered hard in practice. The most rapid bootstrapping on a GSW-based scheme has been presented by Ducas and Micciancio in 2015. In this thesis, we propose a new variant of the scheme proposed by Ducas and Micciancio, that we call TFHE.The TFHE scheme improves previous results, by performing a faster bootstrapping (in the range of a few milliseconds) and by using smaller bootstrapping keys, for the same security level. TFHE uses TLWE and TGSW ciphertexts (both scalar and ring): the acceleration of bootstrapping is mainly due to the replacement of the internal GSW product, used in the majority of previous constructions, with an external product between TLWE and TGSW.Two kinds of bootstrapping are presented. The first one, called gate bootstrapping, is performed after the evaluation of a homomorphic gate (binary or Mux); the second one, called circuit bootstrapping, can be executed after the evaluation of a larger number of homomorphic operations, in order to refresh the result or to make it compatible with the following computations.In this thesis, we also propose new techniques to improve homomorphic computations without bootstrapping and new packing techniques. In particular, we present a vertical packing, that can be used to efficiently evaluate look-up tables, we propose an evaluation via weighted deterministic automata, and we present a homomorphic counter, called TBSR, that can be used to evaluate arithmetic functions.During the thesis, the TFHE scheme has been implemented and it is available in open source.The thesis contains also ancillary works. The first one concerns the study of the first model of post-quantum electronic voting based on fully homomorphic encryption, the second one analyzes the security of homomorphic encryption in a practical cloud implementation scenario, and the third one opens up about a different solution for secure computing, multi-party computation.

Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. Promisingly advanced cryptography technologies such as Homomorphic Encryption (HE) and Confidential Computing (CC) can be utilized to enhance the security and privacy of FL. However, the development of a framework that seamlessly combines these technologies together to provide confidential FL while retaining efficiency remains an ongoing challenge. In this degree project, we develop a lightweight and user-friendly FL framework called Heflp, which integrates HE and CC to ensure data confidentiality and integrity throughout the entire FL lifecycle. Heflp supports four HE schemes to fit diverse user requirements, comprising three pre-existing schemes and one optimized scheme that we design, named Flashev2, which achieves the highest time and spatial efficiency across most scenarios. The time and memory overheads of all four HE schemes are also evaluated and a comparison between the pros and cons of each other is summarized. To validate the effectiveness, Heflp is tested on the MNIST dataset and the Threat Intelligence dataset provided by CanaryBit, and the results demonstrate that it successfully preserves data privacy without compromising model accuracy. / Federated Learning (FL), en variant av Maskininlärning (ML)-teknologi, har framträtt som en dominerande metod för flera parter att samarbeta om att distribuerat träna ML-modeller med hjälp av en central server som vanligtvis tillhandahålls av en molntjänstleverantör (CSP). Trots detta utgör många befintliga sårbarheter ett hot mot FL:s fördelar och medför potentiella risker för datasäkerhet och integritet, såsom läckage av data, missbruk av den centrala servern eller risken för avlyssnare som olagligt söker känslig information. Lovande avancerade kryptoteknologier som Homomorf Kryptering (HE) och Konfidentiell Beräkning (CC) kan användas för att förbättra säkerheten och integriteten för FL. Utvecklingen av en ramverk som sömlöst kombinerar dessa teknologier för att erbjuda konfidentiellt FL med bibehållen effektivitet är dock fortfarande en pågående utmaning. I detta examensarbete utvecklar vi en lättviktig och användarvänlig FL-ramverk som kallas Heflp, som integrerar HE och CC för att säkerställa datakonfidentialitet och integritet under hela FLlivscykeln. Heflp stöder fyra HE-scheman för att passa olika användarbehov, bestående av tre befintliga scheman och ett optimerat schema som vi designar, kallat Flashev2, som uppnår högsta tids- och rumeffektivitet i de flesta scenarier. Tids- och minneskostnaderna för alla fyra HE-scheman utvärderas också, och en jämförelse mellan fördelar och nackdelar sammanfattas. För att validera effektiviteten testas Heflp på MNIST-datasetet och Threat Intelligence-datasetet som tillhandahålls av CanaryBit, och resultaten visar att det framgångsrikt bevarar datasekretessen utan att äventyra modellens noggrannhet.

Everyone is in need for their own privacy and data protection, since encryption transmission was becoming common. Fully Homomorphic Encryption (FHE) has received increased attention because of its capability to execute calculations over the encoded domain. Through using FHE approach, model training can be properly outsourced. The goal of FHE is to enable computations on encrypted files without decoding aside from the end outcome. The CKKS scheme is used in FHE.Network threats are serious danger to credential information, which enable an unauthorised user to extract important and sensitive data by evaluating the information of computations done on raw data. Thus the study provided an efficient solution to the problem of privacy protection in data-driven applications using Machine Learning. The study used an encrypted NSL KDD dataset. Machine learning-based techniques have emerged as a significant trend for detecting malicious attack. Thus, Random Forest (RF) is proposed for the detection of malicious attacks on Homomorphic encrypted data in the cloud server. Logistic Regression (LR) machine learning model is used to predict encrypted data on cloud server. Regardless of the distributed setting, the technique may retain the accuracy and integrity of the previous methods to obtain the final results.

雙方相等性驗證即是在不洩漏任何自身私密資訊的情況下，進行秘密計算來了解彼此的資訊是否相等。然而在大多數的現有協議之中，多數為不公平的協定，也就是說其中的一方(被告知方)只能相信另一方(告知方)所告知的比較結果，而無從驗證。雖然邱等學者在2011 年提出的〝具隱私保護功能之兩方相等性驗證機制之提案〞已經提供了具雙方驗證的協定，但此方案因為在加密演算法上的限制導致實作較為困難。因此，在本論文中，將利用ElGamal 的加密機制，提出了一套新的雙方相等性驗證的協議，具備相同的雙方相等性驗證的功能，但對加密演算法的限制較少，實作及運算也較為有效率。另外，搭配模糊傳輸的協定，讓使用者藉由本研究所提出的協定跟伺服器端溝通，來獲得所欲取得的資料，並同時保障使用者以及伺服器端的隱私。同時除了理論的證明安全性及正確性之外，也撰寫程式模擬並證實協定的正確性及討論其效能。 / Two-party equality testing protocol allows two entities to compare their secrete information without leaking any information except the comparison result. In previous works, the comparison result can only be obtained by one entity (ie. informer) and then the entity informs the result to the other entity (ie. receiver). The receiver has to accept the received result since he has no way to verify its correctness. Ciou et al. in 2011 first mentioned this problem and proposed a new protocol to solve the aforementioned problem. However, their protocol has some specific restrictions which making it unpractical. In this paper, based on the ElGamal encryption, we propose a new two-party equality testing protocol. Our protocol has the same feature (ie. allows the two entries to test the correctness of the comparison result) as Ciou et al.’s protocol but is more efficient and practical than theirs. On the other hand, combining our protocol with an oblivious transfer protocol can let users communicate with servers and to get the data in a private way. It is useful on the issue of privacy protection. Finally, the security and correctness are discussed and proved. The efficiency of the protocol is also provided.

Voting is a fundamental decision making instrument in any consensus-based society. It is employed in various applications from student body elections, reality television shows, shareholder meetings, to national elections. With the motivation of better eciency, scalability, speed, and lower cost, voting is currently shifting from paper-based to the use of electronic medium. This is while aiming to achieve better security, such that voting result reflects true opinions of the voters. Our research focuses on the study of cryptographic voting protocols accommodating a flexible ballot structure as a foundation for building a secure electronic voting system with acceptable voting results. In particular, we search for a solution suitable for the preferential voting system employed in the Australian Federal Election. The outcomes of the research include: improvements and applications of batch proof and verication theorems and techniques, a proposed alternative homomorphic encryption based voting scheme, a proposed Extended Binary Mixing Gate (EBMG) mix-network scheme, a new threshold randomisation technique to achieve receipt-freeness property in voting, and the application of cryptographic voting protocol for preferential voting. The threats and corresponding requirements for a secure secret-ballot voting scheme are rst discussed. There are significant security concerns about the conduct of electronic voting, and it is essential that the voting results re ect the true opinions of the voters - especially in political elections. We examine and extend batch processing proofs and verifications theorems and proposed applications of the theorems useful for voting. Many instances of similar operations can be processed in a single instance using a batch technique based on one of the batch theorems. As the proofs and verications provide formal assurances that the voting process is secure, batch processing offers great efficiency improvements while retaining the security required in a real-world implementation of the protocol. The two main approaches in cryptographic voting protocols, homomorphic encryption based voting and mix-network based voting, are both studied in this research. An alternative homomorphic voting scheme using multiplicative homomorphism property, and a number of novel mix-network schemes are proposed. It is shown that compared to the mix-network approach, homomorphic encryption schemes are not scalable for straight-forward adaptation of preferential systems. One important requirement of secret-ballot voting is receipt-freeness. A randomisation technique to achieve receipt-freeness in voting is examined and applied in an ecient and practical voting scheme employing an optimistic mix-network. A more general technique using threshold randomisation is also proposed. Combination of the primitives, both the homomorphic encryption and mixnetwork approach, yields a hybrid approach producing a secure and ecient secret-ballot voting scheme accommodating a exible ballot structure. The resulting solution oers a promising foundation for secure and practical secret-ballot electronic voting accommodating any type of counting system.