Scaling Distributed Ledgers and Privacy-Preserving Applications

This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of Chainspace, a distributed ledger that supports user defined smart contracts and execute user-supplied transactions on their objects. The correct execution of smart contract transactions is publicly verifiable. Chainspace is scalable by sharding state; it is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT). This thesis also introduces a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols; they allow an attacker, with network access only, to double-spend resources with minimal efforts. We then build Byzcuit, a new cross-shard consensus protocol that is immune to those attacks and that is tailored to run at the heart of Chainspace. Next, we propose FastPay, a high-integrity settlement system for pre-funded payments that can be used as a financial side-infrastructure for Chainspace to support low-latency retail payments. This settlement system is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus). The resulting system has extremely low-latency for both confirmation and payment finality. Finally, this thesis proposes Coconut, a selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. It ensures authenticity and availability even when a subset of credential issuing authorities are malicious or offline, and natively integrates with Chainspace to enable a number of scalable privacy-preserving applications

The Scope and Scale of Clinical Research Accomplished by Rheumatologists Early in Their Careers

[Excerpt] The scope and scale of clinical research is unknown for any medical or surgical specialty beyond snap shots of the broad aims and expenditures of research programs sponsored by federal agencies or the pharmaceutical industry. As a consequence, the workforce and workplace for clinical investigation is enigmatic and unexamined even after explicit warnings that an essential arm for advancing clinical practice is disabled. The present study was designed to examine the nature and extent of investigative activity prevailing among rheumatologists early in their careers. This assessment provides a lens on: i) the fraction of early career rheumatologists who engage in investigative rheumatology, ii) the scope and scale of research in musculoskeletal diseases, iii) funding available for investigative work, iv) the impact of research-intensive institutions, and NIH-K-series awards on research, and v) the demographic backgrounds of early career rheumatologists. The results provide important new insights about the early career workforce for discovery and innovation in rheumatology. The findings integrate demographic, normative, and predictive data to provide the first estimate of the scope and scale of clinical investigation within rheumatology. The results also justify interventions for promoting investigative work, and ultimately advancing the clinical practice of rheumatology

From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality

The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems

Public Accountability: Performance Measurement, The Extended State, And The Search For Trust

In an Academy partnership with the Kettering Foundation, National Academy of Pubic Administration Fellows Melvin J. Dubnick and H. George Frederickson have completed a study of accountability. The study, Public Accountability: Performance Measurement, The Extended State, and the Search for Trust, is a treatment of the strengths and weaknesses of contemporary applications of accountability to public affairs. The working title of the study was Public Accountability: From Ambulance Chasing to Accident Prevention, but that title was thought to lack the dignity such an important subject deserves. Dubnick and Frederickson challenge the often assumed relationship between performance measurement and accountability. They give special attention to accountability challenges associated with the outsourcing of government work, what they call the Extended State. And, they provide examples of effective public accountability in the context of high trust public-private partnerships

Rigorous Design of Distributed Transactions

Database replication is traditionally envisaged as a way of increasing fault-tolerance and availability. It is advantageous to replicate the data when transaction workload is predominantly read-only. However, updating replicated data within a transactional framework is a complex affair due to failures and race conditions among conflicting transactions. This thesis investigates various mechanisms for the management of replicas in a large distributed system, formalizing and reasoning about the behavior of such systems using Event-B. We begin by studying current approaches for the management of replicated data and explore the use of broadcast primitives for processing transactions. Subsequently, we outline how a refinement based approach can be used for the development of a reliable replicated database system that ensures atomic commitment of distributed transactions using ordered broadcasts. Event-B is a formal technique that consists of describing rigorously the problem in an abstract model, introducing solutions or design details in refinement steps to obtain more concrete specifications, and verifying that the proposed solutions are correct. This technique requires the discharge of proof obligations for consistency checking and refinement checking. The B tools provide significant automated proof support for generation of the proof obligations and discharging them. The majority of the proof obligations are proved by the automatic prover of the tools. However, some complex proof obligations require interaction with the interactive prover. These proof obligations also help discover new system invariants. The proof obligations and the invariants help us to understand the complexity of the problem and the correctness of the solutions. They also provide a clear insight into the system and enhance our understanding of why a design decision should work. The objective of the research is to demonstrate a technique for the incremental construction of formal models of distributed systems and reasoning about them, to develop the technique for the discovery of gluing invariants due to prover failure to automatically discharge a proof obligation and to develop guidelines for verification of distributed algorithms using the technique of abstraction and refinement