Declarative design and enforcement for secure cloud applications

The growing demands of users and industry have led to an increase in both size and complexity of deployed software in recent years. This tendency mainly stems from a growing number of interconnected mobile devices and from the huge amounts of data that is collected every day by a growing number of sensors and interfaces. Such increase in complexity imposes various challenges -- not only in terms of software correctness, but also with respect to security. This thesis addresses three complementary approaches to cope with the challenges: (i) appropriate high-level abstractions and verifiable translation methods to executable applications in order to guarantee flawless implementations, (ii) strong cryptographic mechanisms in order to realize the desired security goals, and (iii) convenient methods in order to incentivize the correct usage of existing techniques and tools. In more detail, the thesis presents two frameworks for the declarative specification of functionality and security, together with advanced compilers for the verifiable translation to executable applications. Moreover, the thesis presents two cryptographic primitives for the enforcement of cloud-based security properties: homomorphic message authentication codes ensure the correctness of evaluating functions over data outsourced to unreliable cloud servers; and efficiently verifiable non-interactive zero-knowledge proofs convince verifiers of computation results without the verifiers having access to the computation input.Die wachsenden Anforderungen von Seiten der Industrie und der Endbenutzer verlangen nach immer komplexeren Softwaresystemen -- größtenteils begründet durch die stetig wachsende Zahl mobiler Geräte und die damit wachsende Zahl an Sensoren und erfassten Daten. Mit wachsender Software-Komplexität steigen auch die Herausforderungen an Korrektheit und Sicherheit. Die vorliegende Arbeit widmet sich diesen Herausforderungen in Form dreier komplementärer Ansätze: (i) geeignete Abstraktionen und verifizierbare Übersetzungsmethoden zu ausführbaren Anwendungen, die fehlerfreie Implementierungen garantieren, (ii) starke kryptographische Mechanismen, um die spezifizierten Sicherheitsanforderungen effizient und korrekt umzusetzen, und (iii) zweckmäßige Methoden, die eine korrekte Benutzung existierender Werkzeuge und Techniken begünstigen. Diese Arbeit stellt zwei neuartige Abläufe vor, die verifizierbare Übersetzungen von deklarativen Spezifikationen funktionaler und sicherheitsrelevanter Ziele zu ausführbaren Cloud-Anwendungen ermöglichen. Darüber hinaus präsentiert diese Arbeit zwei kryptographische Primitive für sichere Berechnungen in unzuverlässigen Cloud-Umgebungen. Obwohl die Eingabedaten der Berechnungen zuvor in die Cloud ausgelagert wurden und zur Verifikation der Berechnungen nicht mehr zur Verfügung stehen, ist es möglich, die Korrektheit der Ergebnisse in effizienter Weise zu überprüfen

Efficient Zero-Knowledge Proofs on Signed Data with Applications to Verifiable Computation on Data Streams

We study the problem of privacy-preserving proofs on streamed authenticated data. In this setting, a server receives a continuous stream of data from a trusted data provider, and is requested to prove computations over the data to third parties in a correct and private way. In particular, the third party learns no information on the data beyond the validity of claimed results. A challenging requirement here, is that the third party verifies the validity with respect to the specific data authenticated by the provider, while communicating only with the server. This problem is motivated by various application areas, ranging from stock-market monitoring and prediction services; to the publication of government-ran statistics on large healthcare databases. All of these applications require a reliable and scalable solution, in order to see practical adoption. In this paper, we identify and formalize a key primitive allowing one to achieve the above: homomorphic signatures which evaluate non-deterministic computations (HSNP). We provide a generic construction for an HSNP evaluating universal relations; instantiate the construction; and implement a library for HSNP. This in turn allows us to build SPHINX: a system for proving arbitrary computations over streamed authenticated data in a privacy-preserving manner. SPHINX improves significantly over alternative solutions for this model. For instance, compared to corresponding solutions based on Marlin (Eurocrypt\u2720), the proof generation of SPHINX is between $15\times$ and $1\,300\times$ faster for various computations used in sliding-window statistics

On Provable Security for Complex Systems

We investigate the contribution of cryptographic proofs of security to a systematic security engineering process. To this end we study how to model and prove security for concrete applications in three practical domains: computer networks, data outsourcing, and electronic voting. We conclude that cryptographic proofs of security can benefit a security engineering process in formulating requirements, influencing design, and identifying constraints for the implementation

Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that-while solutions have been suggested to some extent-are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table

Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that - while solutions have been suggested to some extent - are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected

Data exploitation and privacy protection in the era of data sharing

As the amount, complexity, and value of data available in both private and public sectors has risen sharply, the competing goals of data privacy and data utility have challenged both organizations and individuals. This dissertation addresses both goals. First, we consider the task of {\it interorganizational data sharing}, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. A key challenge in this type of scenario is that each organization uses its own set of proprietary, intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving confidentiality of both data records and attributes. We introduce the novel technique of \textit{attribute-based encryption with oblivious attribute translation (OTABE)}, which plays a crucial role in our solution and may prove useful in other applications. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases. Next, we turn our attention to utility that can be derived from the vast and growing amount of data about individuals that is available on social media. As social networks (SNs) continue to grow in popularity, it is essential to understand what can be learned about personal attributes of SN users by mining SN data. The first SN-mining problem we consider is how best to predict the voting behavior of SN users. Prior work only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel type of Bayesian-network (BN) model that combines demographic, behavioral, and social features. We test our method in a predictive analysis of the 2016 U.S. Presidential election. Our work is the first to take a semi-supervised approach in this setting. Using the Expectation-Maximization (EM) algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets and addressing self-selection bias. The second SN-mining challenge we address is the extent to which Dynamic Bayesian Networks (DBNs) can infer dynamic behavioral intentions such as the intention to get a vaccine or to apply for a loan. Knowledge of such intentions has great potential to improve the design of recommendation systems, ad-targeting mechanisms, public-health campaigns, and other social and commercial endeavors. We focus on the question of how to infer an SN user\u27s \textit{offline} decisions and intentions using only the {\it public} portions of her \textit{online} SN accounts. Our contribution is twofold. First, we use BNs and several behavioral-psychology techniques to model decision making as a complex process that both influences and is influenced by static factors (such as personality traits and demographic categories) and dynamic factors (such as triggering events, interests, and emotions). Second, we explore the extent to which temporal models may assist in the inference task by representing SN users as sets of DBNs that are built using our modeling techniques. The use of DBNs, together with data gathered in multiple waves, has the potential to improve both inference accuracy and prediction accuracy in future time slots. It may also shed light on the extent to which different factors influence the decision-making process

Harnessing the Power of Distributed Computing: Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security

Data explosion poses lot of challenges to the state-of-the art systems, applications, and methodologies. It has been reported that 181 zettabytes of data are expected to be generated in 2025 which is over 150\% increase compared to the data that is expected to be generated in 2023. However, while system manufacturers are consistently developing devices with larger storage spaces and providing alternative storage capacities in the cloud at affordable rates, another key challenge experienced is how to effectively process the fraction of large scale of stored data in time-critical conventional systems. One transformative paradigm revolutionizing the processing and management of these large data is distributed computing whose application requires deep understanding. This dissertation focuses on exploring the potential impact of applying efficient distributed computing concepts to long existing challenges or issues in (i) a widely data-intensive scientific application (ii) applying homomorphic encryption to data intensive workloads found in outsourced databases and (iii) security of tokenized incentive mechanism for Federated learning (FL) systems.The first part of the dissertation tackles the Microelectrode arrays (MEAs) parameterization problem from an orthogonal viewpoint enlightened by algebraic topology, which allows us to algebraically parametrize MEAs whose structure and intrinsic parallelism are hard to identify otherwise. We implement a new paradigm, namely Parma, to demonstrate the effectiveness of the proposed approach and report how it outperforms the state-of-the-practice in time, scalability, and memory usage.The second part discusses our work on introducing the concept of parallel caching of secure aggregation to mitigate the performance overhead incurred by the HE module in outsourced databases. The key idea of this optimization approach is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. A new radix HE algorithm was designed and applied to both batch and incremental HE schemes, and experiments carried out on six workloads show that the proposed caching boost state-of-the-art HE schemes by high orders of magnitudes.In the third part, I will discuss our work on leveraging the security benefit of blockchains to enhance or protect the fairness and reliability of tokenized incentive mechanism for FL systems. We designed a blockchain-based auditing protocol to mitigate Gaussian attacks and carried out experiments with multiple FL aggregation algorithms, popular data sets and a variety of scales to validate its effectiveness

Communication-Efficient Probabilistic Algorithms: Selection, Sampling, and Checking

Diese Dissertation behandelt drei grundlegende Klassen von Problemen in Big-Data-Systemen, für die wir kommunikationseffiziente probabilistische Algorithmen entwickeln. Im ersten Teil betrachten wir verschiedene Selektionsprobleme, im zweiten Teil das Ziehen gewichteter Stichproben (Weighted Sampling) und im dritten Teil die probabilistische Korrektheitsprüfung von Basisoperationen in Big-Data-Frameworks (Checking). Diese Arbeit ist durch einen wachsenden Bedarf an Kommunikationseffizienz motiviert, der daher rührt, dass der auf das Netzwerk und seine Nutzung zurückzuführende Anteil sowohl der Anschaffungskosten als auch des Energieverbrauchs von Supercomputern und der Laufzeit verteilter Anwendungen immer weiter wächst. Überraschend wenige kommunikationseffiziente Algorithmen sind für grundlegende Big-Data-Probleme bekannt. In dieser Arbeit schließen wir einige dieser Lücken. Zunächst betrachten wir verschiedene Selektionsprobleme, beginnend mit der verteilten Version des klassischen Selektionsproblems, d. h. dem Auffinden des Elements von Rang $k$ in einer großen verteilten Eingabe. Wir zeigen, wie dieses Problem kommunikationseffizient gelöst werden kann, ohne anzunehmen, dass die Elemente der Eingabe zufällig verteilt seien. Hierzu ersetzen wir die Methode zur Pivotwahl in einem schon lange bekannten Algorithmus und zeigen, dass dies hinreichend ist. Anschließend zeigen wir, dass die Selektion aus lokal sortierten Folgen – multisequence selection – wesentlich schneller lösbar ist, wenn der genaue Rang des Ausgabeelements in einem gewissen Bereich variieren darf. Dies benutzen wir anschließend, um eine verteilte Prioritätswarteschlange mit Bulk-Operationen zu konstruieren. Später werden wir diese verwenden, um gewichtete Stichproben aus Datenströmen zu ziehen (Reservoir Sampling). Schließlich betrachten wir das Problem, die global häufigsten Objekte sowie die, deren zugehörige Werte die größten Summen ergeben, mit einem stichprobenbasierten Ansatz zu identifizieren. Im Kapitel über gewichtete Stichproben werden zunächst neue Konstruktionsalgorithmen für eine klassische Datenstruktur für dieses Problem, sogenannte Alias-Tabellen, vorgestellt. Zu Beginn stellen wir den ersten Linearzeit-Konstruktionsalgorithmus für diese Datenstruktur vor, der mit konstant viel Zusatzspeicher auskommt. Anschließend parallelisieren wir diesen Algorithmus für Shared Memory und erhalten so den ersten parallelen Konstruktionsalgorithmus für Aliastabellen. Hiernach zeigen wir, wie das Problem für verteilte Systeme mit einem zweistufigen Algorithmus angegangen werden kann. Anschließend stellen wir einen ausgabesensitiven Algorithmus für gewichtete Stichproben mit Zurücklegen vor. Ausgabesensitiv bedeutet, dass die Laufzeit des Algorithmus sich auf die Anzahl der eindeutigen Elemente in der Ausgabe bezieht und nicht auf die Größe der Stichprobe. Dieser Algorithmus kann sowohl sequentiell als auch auf Shared-Memory-Maschinen und verteilten Systemen eingesetzt werden und ist der erste derartige Algorithmus in allen drei Kategorien. Wir passen ihn anschließend an das Ziehen gewichteter Stichproben ohne Zurücklegen an, indem wir ihn mit einem Schätzer für die Anzahl der eindeutigen Elemente in einer Stichprobe mit Zurücklegen kombinieren. Poisson-Sampling, eine Verallgemeinerung des Bernoulli-Sampling auf gewichtete Elemente, kann auf ganzzahlige Sortierung zurückgeführt werden, und wir zeigen, wie ein bestehender Ansatz parallelisiert werden kann. Für das Sampling aus Datenströmen passen wir einen sequentiellen Algorithmus an und zeigen, wie er in einem Mini-Batch-Modell unter Verwendung unserer im Selektionskapitel eingeführten Bulk-Prioritätswarteschlange parallelisiert werden kann. Das Kapitel endet mit einer ausführlichen Evaluierung unserer Aliastabellen-Konstruktionsalgorithmen, unseres ausgabesensitiven Algorithmus für gewichtete Stichproben mit Zurücklegen und unseres Algorithmus für gewichtetes Reservoir-Sampling. Um die Korrektheit verteilter Algorithmen probabilistisch zu verifizieren, schlagen wir Checker für grundlegende Operationen von Big-Data-Frameworks vor. Wir zeigen, dass die Überprüfung zahlreicher Operationen auf zwei „Kern“-Checker reduziert werden kann, nämlich die Prüfung von Aggregationen und ob eine Folge eine Permutation einer anderen Folge ist. Während mehrere Ansätze für letzteres Problem seit geraumer Zeit bekannt sind und sich auch einfach parallelisieren lassen, ist unser Summenaggregations-Checker eine neuartige Anwendung der gleichen Datenstruktur, die auch zählenden Bloom-Filtern und dem Count-Min-Sketch zugrunde liegt. Wir haben beide Checker in Thrill, einem Big-Data-Framework, implementiert. Experimente mit absichtlich herbeigeführten Fehlern bestätigen die von unserer theoretischen Analyse vorhergesagte Erkennungsgenauigkeit. Dies gilt selbst dann, wenn wir häufig verwendete schnelle Hash-Funktionen mit in der Theorie suboptimalen Eigenschaften verwenden. Skalierungsexperimente auf einem Supercomputer zeigen, dass unsere Checker nur sehr geringen Laufzeit-Overhead haben, welcher im Bereich von $2\,\%$ liegt und dabei die Korrektheit des Ergebnisses nahezu garantiert wird

Cryptographic techniques for privacy and access control in cloud-based applications

Digitization is one of the key challenges for today’s industries and society. It affects more and more business areas and also user data and, in particular, sensitive information. Due to its sensitivity, it is important to treat personal information as secure and private as possible yet enabling cloud-based software to use that information when requested by the user. In this thesis, we focus on the privacy-preserving outsourcing and sharing of data, the querying of outsourced protected data, and the usage of personal information as an access control mechanism for rating platforms, which should be protected from coercion attacks. In those three categories, we present cryptographic techniques and protocols that push the state of the art. In particular, we first present multi-client oblivious RAM (ORAM), which augments standard ORAM with selective data sharing through access control, confidentiality, and integrity. Second, we investigate on recent work in frequency-hiding order-preserving encryption and show that the state of the art misses rigorous treatment, allowing for simple attacks against the security of the existing scheme. As a remedy, we show how to fix the security definition and that the existing scheme, slightly adapted, fulfills it. Finally, we design and develop a coercion-resistant rating platform. Coercion-resistance has been dealt with mainly in the context of electronic voting yet also affects other areas of digital life such as rating platforms.Die Digitalisierung ist eine der größten Herausforderungen für Industrie und Gesellschaft. Neben vielen Geschäftsbereichen betrifft diese auch, insbesondere sensible, Nutzerdaten. Daher sollten persönliche Informationen so gut wie möglich gesichert werden. Zugleich brauchen Cloud-basierte Software-Anwendungen, die der Nutzer verwenden möchte, Zugang zu diesen Daten. Diese Dissertation fokussiert sich auf das sichere Auslagern und Teilen von Daten unter Wahrung der Privatsphäre, auf das Abfragen von geschützten, ausgelagerten Daten und auf die Nutzung persönlicher Informationen als Zugangsberechtigung für erpressungsresistente Bewertungsplattformen. Zu diesen drei Themen präsentieren wir kryptographische Techniken und Protokolle, die den Stand der Technik voran treiben. Der erste Teil stellt Multi-Client Oblivious RAM (ORAM) vor, das ORAM durch die Möglichkeit, Daten unter Wahrung von Vertraulichkeit und Integrität mit anderen Nutzern zu teilen, erweitert. Der zweite Teil befasst sich mit Freuquency-hiding Order-preserving Encryption. Wir zeigen, dass dem Stand der Technik eine formale Betrachtung fehlt, was zu Angriffen führt. Um Abhilfe zu schaffen, verbessern wir die Sicherheitsdefinition und beweisen, dass das existierende Verschlüsselungsschema diese durch minimale Änderung erfüllt. Abschließend entwickeln wir ein erpressungsresistentes Bewertungsportal. Erpressungsresistenz wurde bisher hauptsächlich im Kontext von elektronischen Wahlen betrachtet