Designing a secure permissionless distributed ledger (blockchain) that performs on par with centralized payment processors, such as Visa, is a challenging task. Most existing distributed ledgers are unable to scale-out, i.e., to grow their total processing capacity with the number of validators; and those that do, compromise security or decentralization. We present Om-niLedger, a novel scale-out distributed ledger that preserves longterm security under permissionless operation. It ensures security and correctness by using a bias-resistant public-randomness protocol for choosing large, statistically representative shards that process transactions, and by introducing an efficient crossshard commit protocol that atomically handles transactions affecting multiple shards. OmniLedger also optimizes performance via parallel intra-shard transaction processing, ledger pruning via collectively-signed state blocks, and low-latency "trust-butverify" validation for low-value transactions. An evaluation of our experimental prototype shows that OmniLedger's throughput scales linearly in the number of active validators, supporting Visa-level workloads and beyond, while confirming typical transactions in under two seconds.
Abstract-The secret keys of critical network authoritiessuch as time, name, certificate, and software update services -represent high-value targets for hackers, criminals, and spy agencies wishing to use these keys secretly to compromise other hosts. To protect authorities and their clients proactively from undetected exploits and misuse, we introduce CoSi, a scalable witness cosigning protocol ensuring that every authoritative statement is validated and publicly logged by a diverse group of witnesses before any client will accept it. A statement S collectively signed by W witnesses assures clients that S has been seen, and not immediately found erroneous, by those W observers. Even if S is compromised in a fashion not readily detectable by the witnesses, CoSi still guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to risk that the compromise will soon be detected by one of the W witnesses. Because clients can verify collective signatures efficiently without communication, CoSi protects clients' privacy, and offers the first transparency mechanism effective against persistent man-inthe-middle attackers who control a victim's Internet access, the authority's secret key, and several witnesses' secret keys. CoSi builds on existing cryptographic multisignature methods, scaling them to support thousands of witnesses via signature aggregation over efficient communication trees. A working prototype demonstrates CoSi in the context of timestamping and logging authorities, enabling groups of over 8,000 distributed witnesses to cosign authoritative statements in under two seconds.
Deterministic execution offers many benefits for debugging, fault tolerance, and security. Current methods of executing parallel programs deterministically, however, often incur high costs, allow misbehaved software to defeat repeatability, and transform time-dependent races into input-or path-dependent races without eliminating them. We introduce a new parallel programming model addressing these issues, and use Determinator, a proof-of-concept OS, to demonstrate the model's practicality. Determinator's microkernel API provides only "shared-nothing" address spaces and deterministic interprocess communication primitives to make execution of all unprivileged code-well-behaved or notprecisely repeatable. Atop this microkernel, Determinator's user-level runtime adapts optimistic replication techniques to offer a private workspace model for both thread-level and process-level parallel programing. This model avoids the introduction of read/write data races, and converts write/write races into reliably-detected conflicts. Coarse-grained parallel benchmarks perform and scale comparably to nondeterministic systems, on both multicore PCs and across nodes in a distributed cluster.
The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns. MedCo uses (a) collective homomorphic encryption to provide trust decentralization and end-to-end confidentiality protection, and (b) obfuscation techniques to achieve formal notions of privacy, such as differential privacy. A critical feature of MedCo is that it is fully integrated within the i2b2 (Informatics for Integrating Biology and the Bedside) framework, currently used in more than 300 hospitals worldwide. Therefore, it is easily adoptable by clinical sites. We demonstrate MedCo's practicality by testing it on data from The Cancer Genome Atlas in a simulated network of three institutions. Its performance is comparable to the ones of SHRINE (networked i2b2), which, in contrast, does not provide any data protection guarantee.
This paper describes a novel approach to providing modular and extensible operating system functionality and encapsulated environments based on a synthesis of microkernel and virtual machine concepts. We have developed a software-based virtualizable architecture called Fluke that allows recursive virtual machines (virtual machines running on other virtual machines) to be implemented efficiently by a microkernel running on generic hardware. A complete virtual machine interface is provided at each level; efficiency derives from needing to implement only new functionality at each level. This infrastructure allows common OS functionality, such as process management, demand paging, fault tolerance, and debugging support, to be provided by cleanly modularized, independent, stackable virtual machine monitors, implemented as user processes. It can also provide uncommon or unique OS features, including the above features specialized for particular applications' needs, virtual machines transparently distributed cross-node, or security monitors that allow arbitrary untrusted binaries to be executed safely. Our prototype implementation of this model indicates that it is practical to modularize operating systems this way. Some types of virtual machine layers impose almost no overhead at all, while others impose some overhead (typically 0-35%), but only on certain classes of applications.
Timing side-channels represent an insidious security challenge for cloud computing, because: (a) massive parallelism in the cloud makes timing channels pervasive and hard to control; (b) timing channels enable one customer to steal information from another without leaving a trail or raising alarms; (c) only the cloud provider can feasibly detect and report such attacks, but the provider's incentives are not to; and (d) resource partitioning schemes for timing channel control undermine statistical sharing efficiency, and, with it, the cloud computing business model. We propose a new approach to timing channel control, using provider-enforced deterministic execution instead of resource partitioning to eliminate timing channels within a shared cloud domain. Provider-enforced determinism prevents execution timing from affecting the results of a compute task, however large or parallel, ensuring that a task's outputs leak no timing information apart from explicit timing inputs and total compute duration. Experiments with a prototype OS for deterministic cloud computing suggest that such an approach may be practical and efficient. The OS supports deterministic versions of familiar APIs such as processes, threads, shared memory, and file systems, and runs coarse-grained parallel tasks as efficiently and scalably as current timing channel-ridden systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.