Match Me if You Can: Matchmaking Encryption and its Applications

We introduce a new form of encryption that we name matchmaking encryption (ME). Using ME, sender S and receiver R (each with its own attributes) can both specify policies the other party must satisfy in order for the message to be revealed. The main security guarantee is that of privacy-preserving policy matching: During decryption nothing is leaked beyond the fact that a match occurred/did not occur. ME opens up new ways of secretly communicating, and enables several new applications where both participants can specify fine-grained access policies to encrypted data. For instance, in social matchmaking, S can encrypt a file containing his/her personal details and specify a policy so that the file can be decrypted only by his/her ideal partner. On the other end, a receiver R will be able to decrypt the file only if S corresponds to his/her ideal partner defined through a policy. On the theoretical side, we define security for ME, as well as provide generic frameworks for constructing ME from functional encryption. These constructions need to face the technical challenge of simultaneously checking the policies chosen by S and R, to avoid any leakage. On the practical side, we construct an efficient identity-based scheme for equality policies, with provable security in the random oracle model under the standard BDH assumption. We implement and evaluate our scheme and provide experimental evidence that our construction is practical. We also apply identity-based ME to a concrete use case, in particular for creating an anonymous bulletin board over a Tor network

Practical Privacy-Preserving Authentication for SSH

Public-key authentication in SSH reveals more information about the participants\u27 keys than is necessary. (1) The server can learn a client\u27s entire set of public keys, even keys generated for other servers. (2) The server learns exactly which key the client uses to authenticate, and can further prove this fact to a third party. (3) A client can learn whether the server recognizes public keys belonging to other users. Each of these problems lead to tangible privacy violations for SSH users. In this work we introduce a new public-key authentication method for SSH that reveals essentially the minimum possible amount of information. With our new method, the server learns only whether the client knows the private key for some authorized public key. If multiple keys are authorized, the server does not learn which one the client used. The client cannot learn whether the server recognizes public keys belonging to other users. Unlike traditional SSH authentication, our method is fully deniable. Our new method also makes it harder for a malicious server to intercept first-use SSH connections on a large scale. Our method supports existing SSH keypairs of all standard flavors — RSA, ECDSA, EdDSA. It does not require users to generate new key material. As in traditional SSH authentication, clients and servers can use a mixture of different key flavors in a single authentication session. We integrated our new authentication method into OpenSSH, and found it to be practical and scalable. For a typical client and server with at most 10 ECDSA/EdDSA keys each, our protocol requires 9 kB of communication and 12.4 ms of latency. Even for a client with 20 keys and server with 100 keys, our protocol requires only 12 kB of communication and 26.7 ms of latency

Decentralizing Software Identity Management

Software ist in unterschiedlichsten Bereichen von größter Wichtigkeit: Wirtschaft, Handel, Industrielle Steueranlagen, Transport, Logistik, Kommunikation, sowie im privaten Gebrauch um nur einige Beispiele zu nennen. Es ist entsprechend unverzichtbar, Software mit Integrität und einer expliziten Befürwortung durch den jeweiligen Entwickler oder Herausgeber zu beziehen. In dieser Arbeit verfolgen wir das Ziel, die Interaktion zwischen Erstellern und Nutzern von Software durch die Etablierung und Nutzung von expliziten Identitäten für Software weiter abzusichern. Eine Softwareidentität etabliert in erster Linie einen eindeutigen und persistenten Bezugspunkt an den Softwareersteller Informationen zu Binärdateien ihrer Software anhängen und entfernen können. Die Möglichkeit zuvor veröffentlichte Binärdateien aus einer Softwareidentität zu entfernen erlaubt Entwicklern auf sicherheitskritische Fehler oder Kompromittierungen zu reagieren, indem sie klar kommunizieren, dass bestimmte Binärdateien nicht länger verwendet werden sollten. Nutzer einer Software können über solche Widerrufe oder neue Versionen informiert werden, indem sie die entsprechende Softwareidentität beobachten über die sie dann auch die Integrität und Befürwortung von heruntergeladenen Binärdateien überprüfen können. Distributed Ledger Technologien wie Ethereum oder zuvor Bitcoin scheinen taugliche Plattformen für die Umsetzung von Softwareidentitäten zu sein, ohne dabei auf zentrale Anbieter vertrauen zu müssen. Ein offenes Peer-to-Peer Netzwerk etabliert einen Konsens über einen manipulationsgeschützten Zustandsverlauf, der namensgebende Ledger, und ermöglicht Zugriff auf selbigen. Ethereum ist einer der ersten Distributed Ledger, der sogenannte Smart Contracts ermöglicht. Dabei handelt es sich um Programme, die auf einem Distributed Ledger installiert und ausgeführt werden und damit einen eindeutig referenzierbaren Teil des Ledgerzustandes etablieren und verwalten. Einzig und allein die Programmierung eines Smart Contracts bestimmt darüber, wer den Teilzustand wann und wie verändern kann. Die erste Forschungsfrage dieser Dissertation zielt auf die Tauglichkeit von Distributed Ledger Technologien hinsichtlich der Etablierung, Verwaltung, und Nutzung von Softwareidentitäten ab. Insbesondere untersuchen wir, wie nützliche Eigenschaften für Softwareidentitätsmanagement und -nutzung von den Sicherheitseigenschaften des zugrundeliegenden Distributed Ledgers und weiteren Annahmen abgeleitet werden können. Neben der Verwendung von Softwareidentitäten zur weiteren Absicherung der Softwaredistribution untersuchen wir außerdem ihre Nutzbarkeit als Grundlage für unabhängige Begutachtungen von Softwareversionen. Die Durchführung solcher unabhängigen Begutachtungen mittels Distributed Ledgern führt unweigerlich zu einer Herausforderung hinsichtlich der koordinierten Offenlegung der Ergebnisse. Zum Zeitpunkt der Abfassung dieser Arbeit bietet kein Distributed Ledger eine entsprechende Funktionalität, um die Erstellung einer Menge unabhängig erstellter Aussagen zu unterstützen oder zu dokumentieren. Die zweite Forschungsfrage dieser Arbeit befasst sich deshalb mit der Umsetzung eines Offenlegungsmechanismus für Distributed Ledger basierend auf bestehenden kryptografischen Primitiven. Wir behandeln beide Forschungsfragen, indem wir entsprechende dezentrale Anwendungen konzipieren, implementieren, und evaluieren. Wir nutzen dabei Ethereum als prominentestes Exemplar eines Smart-Contract-fähigen Distributed Ledgers. Genauer gesagt messen wir die Installations- und Ausführungskosten jener Smart Contracts, die für unsere dezentralen Anwendungen nötig sind, um ihre praktische Tauglichkeit zu bestimmen. In zwei Fällen ermitteln wir außerdem den Rechenaufwand, der abseits des Ledgers anfällt. Wir zeigen zudem semi-formal, wie die Sicherheitseigenschaften unserer Proof of Concept Implementierung von dem zugrundeliegenden Distributed Ledger und weiteren Annahmen abgeleitet werden können. Wir kommen zu dem Ergebnis, dass Ethereum stellvertretend für Smart-Contract-fähige Distributed Ledger eine taugliche Plattform für die Umsetzung von Softwareidentitäten ist, inklusive der zuvor angemerkten unabhängigen Begutachtungen. Da unser Konzept des Softwareidentitätsmanagements auf eher grundlegenden Eigenschaften von Distributed Ledgern fußt sollte es sich gut auf andere Systeme übertragen lassen. Im Gegensatz dazu erfordert unser Konzept für einen Offenlegungsmechanismus die Unterstützung von bestimmten kryptografischen Operationen auf dem verwendeten Ledger, was die Übertragbarkeit entsprechend einschränkt. Die Kosten für die Installation der nötigen Smart Contracts sind signifikant größer als die Ausführungskosten im typischen Gebrauch, weshalb wir für zukünftige Arbeit empfehlen, die Wiederverwendbarkeit von installierten Smart Contract Instanzen zu verbessern. Bei der koordinierten Offenlegung von unabhängig erstellten Aussagen auf einem Distributed Ledger erzielen wir eine Reduktion der Gesamtkosten von 20–40 % im Vergleich zu verwandter Arbeit, indem wir unterschiedliche kryptografische Anforderungen ausnutzen. Unser Ansatz um eine koordinierte Offenlegung auf Ethereum zu erzielen stützt sich auf Elliptische-Kurven-Operationen die, obwohl ausreichend, zum aktuellen Zeitpunkt sehr eingeschränkt sind. Entsprechend trägt unsere Arbeit einen weiteren Grund für die Erweiterung der unterstützten elliptischen Kurven im Zuge der Weiterentwicklung von Ethereum bei

Security and Privacy Preservation in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a compelling paradigm that enables a crowd of individuals to cooperatively collect and share data to measure phenomena or record events of common interest using their mobile devices. Pairing with inherent mobility and intelligence, mobile users can collect, produce and upload large amounts of data to service providers based on crowdsensing tasks released by customers, ranging from general information, such as temperature, air quality and traffic condition, to more specialized data, such as recommended places, health condition and voting intentions. Compared with traditional sensor networks, MCS can support large-scale sensing applications, improve sensing data trustworthiness and reduce the cost on deploying expensive hardware or software to acquire high-quality data. Despite the appealing benefits, however, MCS is also confronted with a variety of security and privacy threats, which would impede its rapid development. Due to their own incentives and vulnerabilities of service providers, data security and user privacy are being put at risk. The corruption of sensing reports may directly affect crowdsensing results, and thereby mislead customers to make irrational decisions. Moreover, the content of crowdsensing tasks may expose the intention of customers, and the sensing reports might inadvertently reveal sensitive information about mobile users. Data encryption and anonymization techniques can provide straightforward solutions for data security and user privacy, but there are several issues, which are of significantly importance to make MCS practical. First of all, to enhance data trustworthiness, service providers need to recruit mobile users based on their personal information, such as preferences, mobility pattern and reputation, resulting in the privacy exposure to service providers. Secondly, it is inevitable to have replicate data in crowdsensing reports, which may possess large communication bandwidth, but traditional data encryption makes replicate data detection and deletion challenging. Thirdly, crowdsensed data analysis is essential to generate crowdsensing reports in MCS, but the correctness of crowdsensing results in the absence of malicious mobile users and service providers become a huge concern for customers. Finally yet importantly, even if user privacy is preserved during task allocation and data collection, it may still be exposed during reward distribution. It further discourage mobile users from task participation. In this thesis, we explore the approaches to resolve these challenges in MCS. Based on the architecture of MCS, we conduct our research with the focus on security and privacy protection without sacrificing data quality and users' enthusiasm. Specifically, the main contributions are, i) to enable privacy preservation and task allocation, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation. In SPOON, the service provider recruits mobile users based on their locations, and selects proper sensing reports according to their trust levels without invading user privacy. By utilizing the blind signature, sensing tasks are protected and reports are anonymized. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users; ii) to improve communication efficiency while guaranteeing data confidentiality, we propose a fog-assisted secure data deduplication scheme, in which a BLS-oblivious pseudo-random function is developed to enable fog nodes to detect and delete replicate data in sensing reports without exposing the content of reports. Considering the privacy leakages of mobile users who report the same data, the blind signature is utilized to hide users' identities, and chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous greedy mobile users; iii) to achieve data statistics with privacy preservation, we propose a privacy-preserving data statistics scheme to achieve end-to-end security and integrity protection, while enabling the aggregation of the collected data from multiple sources. The correctness verification is supported to prevent the corruption of the aggregate results during data transmission based on the homomorphic authenticator and the proxy re-signature. A privacy-preserving verifiable linear statistics mechanism is developed to realize the linear aggregation of multiple crowdsensed data from a same device and the verification on the correctness of aggregate results; and iv) to encourage mobile users to participating in sensing tasks, we propose a dual-anonymous reward distribution scheme to offer the incentive for mobile users and privacy protection for both customers and mobile users in MCS. Based on the dividable cash, a new reward sharing incentive mechanism is developed to encourage mobile users to participating in sensing tasks, and the randomization technique is leveraged to protect the identities of customers and mobile users during reward claim, distribution and deposit

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest

Trust enhanced security in SaaS cloud computing

Trust problem in Software as a Service Cloud Computing is a broad range of a Data Owner’s concerns about the data in the Cloud. The Data Owner’s concerns about the data arise from the way the data is handled in locations and machines that are unknown to the Data Owner

HPCCP/CAS Workshop Proceedings 1998

This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey

Individual Verifiability for E-Voting, From Formal Verification To Machine Learning

The cornerstone of secure electronic voting protocols lies in the principle of individual verifiability. This thesis delves into the intricate task of harmonizing this principle with two other crucial aspects: ballot privacy and coercion-resistance. In the realm of electronic voting, individual verifiability serves as a critical safeguard. It empowers each voter with the ability to confirm that their vote has been accurately recorded and counted in the final tally. This thesis explores the intricate balance between this pivotal aspect of electronic voting and the equally important facets of ballot privacy and coercion-resistance. Ballot privacy, or the assurance that a voter's choice remains confidential, is a fundamental right in democratic processes. It ensures that voters can express their political preferences without fear of retribution or discrimination. On the other hand, coercion-resistance refers to the system's resilience against attempts to influence or manipulate a voter's choice. Furthermore, this thesis also ventures into an empirical analysis of the effectiveness of individual voter checks in ensuring a correct election outcome. It considers a scenario where an adversary possesses additional knowledge about the individual voters and can strategically decide which voters to target. The study aims to estimate the degree to which these checks can still guarantee the accuracy of the election results under such circumstances. In essence, this thesis embarks on a comprehensive exploration of the dynamics between individual verifiability, ballot privacy, and coercion-resistance in secure electronic voting protocols. It also seeks to quantify the effectiveness of individual voter checks in maintaining the integrity of election outcomes, particularly when faced with a knowledgeable and capable adversary. The first contribution of this thesis is revisiting the seminal coercion-resistant e-voting protocol by Juels, Catalano, and Jakobsson (JCJ), examining its usability and practicality. It discusses the credential handling system proposed by Neumann et al., which uses a smart card to unlock or fake credentials via a PIN code. The thesis identifies several security concerns with the JCJ protocol, including an attack on coercion-resistance due to information leakage from the removal of duplicate ballots. It also addresses the issues of PIN errors and the single point of failure associated with the smart card. To mitigate these vulnerabilities, we propose hardware-flexible protocols that allow credentials to be stored by ordinary means while still being PIN-based and providing PIN error resilience. One of these protocols features a linear tally complexity, ensuring efficiency and scalability for large-scale electronic voting systems. The second contribution of this thesis pertains to the exploration and validation of the ballot privacy definition proposed by Cortier et. al., particularly in the context of an adversarial presence. Our exploration involves both the Selene and the MiniVoting abstract scheme. We apply Cortier's definition of ballot privacy to this scheme, investigating how it holds up under this framework. To ensure the validity of our findings, we employ the use of tools for machine-checked proof. This method provides a rigorous and reliable means of verifying our results, ensuring that our conclusions are both accurate and trustworthy. The final contribution of this thesis is a detailed examination and analysis of the Estonian election results. This analysis is conducted in several phases, each contributing to a comprehensive understanding of the election process. The first phase involves a comprehensive marginal analysis of the Estonian election results. We compute upper bounds for several margins, providing a detailed statistical overview of the election outcome. This analysis allows us to identify key trends and patterns in the voting data, laying the groundwork for the subsequent phase of our research. We then train multiple binary classifiers to predict whether a voter is likely to verify their vote. This predictive modeling enables an adversary to gain insights into voter behavior and the factors that may influence their decision to verify their vote. With the insights gained from the previous phases, an adversarial classification algorithm for verifying voters is trained. The likelihood of such an adversary is calculated using various machine learning models, providing a more robust assessment of potential threats to the election process

Privacy-preserving controls for sharing mHealth data

Mobile devices allow people to collect and share health and health-related information with recipients such as health providers, family and friends, employers and insurance companies, to obtain health, emotional or financial benefits. People may consider certain health information sensitive and prefer to disclose only what is necessary. In this dissertation, we present our findings about factors that affect people’s sharing behavior, describe scenarios in which people may wish to collect and share their personal health-related information with others, but may be hesitant to disclose the information if necessary controls are not available to protect their privacy, and propose frameworks to provide the desired privacy controls. We introduce the concept of close encounters that allow users to share data with other people who may have been in spatio-temporal proximity. We developed two smartphone-based systems that leverage stationary sensors and beacons to determine whether users are in spatio-temporal proximity. The first system, ENACT, allows patients diagnosed with a contagious airborne disease to alert others retrospectively about their possible exposure to airborne virus. The second system, SPICE, allows users to collect sensor information, retrospectively, from others with whom they shared a close encounter. We present design and implementation of the two systems, analyse their security and privacy guarantees, and evaluate the systems on various performance metrics. Finally, we evaluate how Bluetooth beacons and Wi-Fi access points can be used in support of these systems for close encounters, and present our experiences and findings from a deployment study on Dartmouth campus

Evidence-based Cybersecurity: Data-driven and Abstract Models

Achieving computer security requires both rigorous empirical measurement and models to understand cybersecurity phenomena and the effectiveness of defenses and interventions. To address the growing scale of cyber-insecurity, my approach to protecting users employs principled and rigorous measurements and models. In this dissertation, I examine four cybersecurity phenomena. I show that data-driven and abstract modeling can reveal surprising conclusions about longterm, persistent problems, like spam and malware, and growing threats like data-breaches and cyber conflict. I present two data-driven statistical models and two abstract models. Both of the data-driven models show that the presence of heavy-tailed distributions can make naive analysis of trends and interventions misleading. First, I examine ten years of publicly reported data breaches and find that there has been no increase in size or frequency. I also find that reported and perceived increases can be explained by the heavy-tailed nature of breaches. In the second data-driven model, I examine a large spam dataset, analyzing spam concentrations across Internet Service Providers. Again, I find that the heavy-tailed nature of spam concentrations complicates analysis. Using appropriate statistical methods, I identify unique risk factors with significant impact on local spam levels. I then use the model to estimate the effect of historical botnet takedowns and find they are frequently ineffective at reducing global spam concentrations and have highly variable local effects. Abstract models are an important tool when data are unavailable. Even without data, I evaluate both known and hypothesized interventions used by search providers to protect users from malicious websites. I present a Markov model of malware spread and study the effect of two potential interventions: blacklisting and depreferencing. I find that heavy-tailed traffic distributions obscure the effects of interventions, but with my abstract model, I showed that lowering search rankings is a viable alternative to blacklisting infected pages. Finally, I study how game-theoretic models can help clarify strategic decisions in cyber-conflict. I find that, in some circumstances, improving the attribution ability of adversaries may decrease the likelihood of escalating cyber conflict