The primary goal of a distributed storage system is to ensure that clients can both write and read data in a reliable and consistent manner, even in the presence of failures. While existing asynchronous verifiable information dispersal (AVID) protocols achieve optimal space complexity for storage and communication complexity for data retrieval in a Byzantine setting, the crucial operations of data dispersal and node recovery have received less attention. We propose an efficient AVID protocol that simultaneously guarantees low complexities for dispersal, storage, retrieval, and recovery. At the core of the proposed protocol lies a novel mechanism to encode data in a two-dimensional matrix and a bespoke dispersal algorithm. The protocol maintains an optimal communication complexity for retrieval while substantially improving upon the state of the art for recovery. Additionally, we describe a protocol variant that offers a reduced space complexity and communication complexity for dispersal, at the expense of a higher communication complexity for recovery and, depending on its parameterization, also retrieval. As the proposed protocols strike a balance across all considered metrics, they are suitable for a broad range of real-world use cases.

The reliable broadcast protocol with the best communication complexity for long messages to date is the MiniCast protocol of Locher & Shoup (2024). To reliably broadcast a message m to n parties, MiniCast has communication complexity ~ 1.5|m|n when the size |m| of m is large. However, the round complexity of MiniCast is 4, which is worse than the 3 rounds of the classical protocol of Bracha. We give a new reliable broadcast protocol whose communication complexity is essentially the same as that of MiniCast, but whose round complexity is just 3. For large |m|, the communication complexity of our new protocol is essentially optimal (for 3-round protocols with somewhat balanced communication). Like MiniCast, our new protocol does not rely on any cryptography other than hash functions. We also give two new 2-round protocols that rely on signatures. The communication complexity of the first protocol is also ~ 1.5|m|n, unless the sender provably misbehaves, in which case its communication complexity may degrade to ~ 2|m|n. The communication complexity of the second protocol is ~ 1.5|m|n, even in the worst case, but (unlike our other protocols) the communication is unbalanced.

There is growing interest in providing programmatic access to the value locked in Bitcoin, which famously offers limited programmability itself. Various approaches have been put forth in recent years, with the vast majority of proposed mechanisms either building new functionality on top of Bitcoin or leveraging a bridging mechanism to enable smart contracts that make use of "wrapped" bitcoins on entirely different platforms.In this work, an architecture is presented that follows a different approach. The architecture enables the execution of Turing-complete Bitcoin smart contracts on the Internet Computer (IC), a blockchain platform for hosting and executing decentralized applications. Instead of using a bridge, IC and Bitcoin nodes interact directly, eliminating potential security risks that the use of a bridge entails. This integration requires novel concepts, in particular to reconcile the probabilistic nature of Bitcoin with the irreversibility of finalized state changes on the IC, which may be of independent interest.In addition to the presentation of the architecture, we provide evaluation results based on measurements of the Bitcoin integration running on mainnet. The evaluation results demonstrate that, with finalization in a few seconds and low execution costs, this integration enables complex Bitcoin-based decentralized applications that were not practically feasible or economically viable before.

Reliable broadcast is a fundamental primitive in distributed computing that is widely used in various applications. Several new reliable broadcast algorithms have been presented in recent years, primarily focusing on reducing the communication complexity, which is the total number of exchanged bits in the worst case. While significant progress has been achieved, all proposed algorithms share a common weakness. Executions may fail, i.e., no message is ever delivered, while incurring a communication complexity equal or nearly equal to the communication complexity of executions where a message is delivered. In fact, a single Byzantine node, acting as the dedicated sender, is sufficient to trigger such executions, causing all nodes to consume bandwidth in vain. This paper introduces the novel concept of a reliable broadcast detector, a distributed algorithm that can be coupled with a reliable broadcast algorithm to minimize the communication complexity of failed executions. Two concrete detectors are presented with different requirements and properties. Additionally, reliable broadcast algorithms that utilize detectors are introduced, the main algorithm guaranteeing an overhead factor, compared to an ideal failure-free execution, that tends to 2 as the network size increases. Furthermore, a lower bound is proven that an overhead factor of 5/3 is inevitable when the sender initially broadcasts the message, as is the case for the proposed algorithm. Therefore, it achieves a bound that is close to optimal for any algorithm with this property.

Byzantine reliable broadcast is a fundamental problem in distributed computing, which has been studied extensively over the past decades. State-of-the-art algorithms are predominantly based on the approach to share encoded fragments of the broadcast message, yielding an asymptotically optimal communication complexity when the message size exceeds the network size, a condition frequently encountered in practice. However, algorithms following the standard coding approach incur an overhead factor of at least 3, which can already be a burden for bandwidth-constrained applications. Minimizing this overhead is an important objective with immediate benefits to protocols that use a reliable broadcast routine as a building block. This paper introduces a novel mechanism to lower the communication and computational complexity. Two algorithms are presented that employ this mechanism to reliably broadcast messages in an asynchronous network where less than a third of all nodes are Byzantine. The first algorithm reduces the overhead factor to 2 and has a time complexity of 3 if the sender is honest, whereas the second algorithm attains an optimal time complexity of 2 with the same overhead factor in the absence of equivocation. Moreover, an optimization is proposed that reduces the overhead factor to 3/2 under normal operation in practice. Lastly, a lower bound is proved that an overhead factor lower than 3/2 cannot be achieved for a relevant class of reliable broadcast algorithms.

A consensus algorithm lies at the core of every distributed ledger as it defines how the constituent parts of the distributed system ensure that they share identical ledger copies. For a broad range of promising applications of distributed ledger technology, the parties responsible for the application can specify and control the distributed entities maintaining the ledger. Since the faulty behavior of any such entity must not cause inconsistencies in the ledger and access to the ledger is restricted, Byzantine agreement is a viable candidate as a backbone for such so-called permissioned distributed ledgers. In this setting, a primary objective is to maximize throughput, i.e., the rate at which transactions can be processed, which requires a quick settlement at the consensus layer.

Numerous applications, e.g., in the industrial sector, produce large amounts of time-series data, which must be stored and made available for distributed processing. While outsourcing data storage and processing to third-party service providers offers many benefits, it raises data privacy issues. In light of this problem, techniques have been proposed to share only encrypted data with the remote service provider, yet the capability to run meaningful queries over the data is preserved. However, timeseries data is typically compressed at the server to save space, which is not easily possible when dealing with encrypted data. Moreover, data must be compressed in such a way that queries can still be executed efficiently. As a first step in this direction, we present an approach that preserves data privacy, enables compression at the server, and supports querying of the stored data. Our evaluation using realworld time-series data shows that our compression mechanism can reduce the required space drastically. Moreover, the median running time of all considered queries increases marginally, implying that compression can be introduced without sacrificing performance of query execution.

There is a growing demand for live, on-the-fly processing of increasingly large amounts of data. In order to ensure the timely and reliable processing of streaming data, a variety of distributed stream processing architectures and platforms have been developed, which handle the fundamental tasks of (dynamically) assigning processing tasks to the currently available physical resources and routing streaming data between these resources. However, while there are plenty of platforms offering such functionality, the theory behind it is not well understood. In particular, it is unclear how to best allocate the processing tasks to the given resources. In this paper, we establish a theoretical foundation by formally defining a task allocation problem for distributed stream processing, which we prove to be NP-hard. Furthermore, we propose an approximation algorithm for the class of series-parallel decomposable graphs, which captures a broad range of common stream processing applications. The algorithm achieves a constant-factor approximation under the assumptions that the number of resources scales at least logarithmically with the number of computational tasks and the computational cost of the tasks dominates the cost of communication.

Homomorphic encryption and secure multi-party computation enable computations on encrypted data. However, both techniques suffer from a large performance overhead. While advances in algorithms might reduce the overhead, we show that achieving perfect (or even computational) confidentiality is not possible without increasing the running time compared to computations on plaintext more than exponentially in some cases. In practice, however, perfect confidentiality is not always required. The paper discusses mechanisms to trade off confidentiality and performance for computing on ciphertexts. It introduces a fine-grained approach to define security levels for variables called (statistical) subdomain privacy. This concept differs substantially from prior work because it treats a variable as confidential or non-confidential depending on the actual value. We further propose privacy-preserving methods for memory access patterns. We apply our techniques to improve performance of control flow logic (loops, if-then-else logic) and arithmetic operations such as multiplications. The evaluation shows that the resulting speedup can be in the order of several magnitudes depending on the privacy needs.

Power companies are deploying a multitude of sensors to monitor the energy grid. Measurements at different locations should be aligned in time to obtain the global state of the grid, and the industry therefore uses GPS as a common clock source. However, these sensors are exposed to GPS time spoofing attacks that cause misaligned aggregated measurements, leading to inaccurate monitoring that affects power stability and line fault contingencies. In this paper, we analyze the resilience of phasor measurement sensors, which record voltages and currents, to GPS spoofing performed by an adversary external to the system. We propose a solution that leverages the characteristics of multiple sensors in the power grid to limit the feasibility of such attacks. In order to increase the robustness of wide-area power grid monitoring, we evaluate mechanisms that allow collaboration among GPS receivers to detect spoofing attacks. We apply multilateration techniques to allow a set of GPS receivers to locate a false GPS signal source. Using simulations, we show that receivers sharing a local clock can locate nearby spoofing adversaries with sufficient confidence.

Tit-for-tat trading lies at the heart of many incentive mechanisms for distributed systems where participants are anonymous. However, since the standard tit-for-tat approach is restricted to bilateral exchanges, data is transferred only between peers with direct and mutual interests. Generalizing tit-for-tat to multi-lateral trades where contributions can occur along cycles of interest may improve the performance of a system in terms of faster downloads without compromising the incentive-compatibility inherent to tit-for-tat trading. In this paper, we study the potential benefits and limitations of such a generalized trading in swarm-based peer-to-peer systems. Extensive simulations are performed to evaluate different techniques and to identify the crucial parameters influencing the obtainable throughput improvements and the corresponding tradeoffs. Moreover, we discuss extensions for overhead reduction and provide an optimized distributed implementation of our techniques. In summary, we find that allowing inter-swarm trades on short trading cycles can improve the throughput significantly; on the other hand, trading on long cycles does not pay off as the communication and management overhead becomes exceedingly large while the additional performance gains are marginal.

In this paper, we study the information exchange problem on a set of multiple access channels: k arbitrary nodes have information they want to distribute to the entire network via a shared medium partitioned into channels. We present algorithms and lower bounds on the time and channel complexity for disseminating these k information items in a single-hop network of n nodes. More precisely, we devise a deterministic algorithm running in asymptotically optimal time O(k) using O(n(log (k)/k)) channels if k less or equal to (1/6) * log n and O(log(1+p) (n/k) channels otherwise, where p>0 is an arbitrarily small constant. In addition, we show that Omega(n(Ω(1/k))+logk n) channels are necessary to achieve this time complexity.

We consider the question of how a conspiring subgroup of peers in a p2p network can find each other and communicate without provoking suspicion among regular peers or an authority that monitors the network. In particular, we look at the problem of how a conspirer can broadcast a message secretly to all fellow conspirers. As a subproblem of independent interest, we study the problem of how a conspirer can safely determine a connected peer's type, i.e., learning whether the connected peer is a conspirer or a regular peer without giving away its own type in the latter case. For several levels of monitoring, we propose distributed and efficient algorithms that transmit hidden information by varying the block request sequence meaningfully. We find that a p2p protocol offers several steganographic channels through which hidden information can be transmitted, and p2p networks are susceptible to hidden communication even if they are completely monitored.

A simple indicator for an anomaly in a network is a rapid increase in the total number of distinct network connections. While it is fairly easy to maintain an accurate estimate of the current total number of distinct connections using streaming algorithms that exhibit both a low space and computational complexity, identifying the network entities that are involved in the largest number of distinct connections efficiently is considerably harder.

This article reports on the results of our measurement study of the Kad network. Although several fully decentralized peer-to-peer systems have been proposed in the literature, most existing systems still employ a centralized architecture. The Kad ne

We study the problem of clock synchronization in highly dynamic networks, where communication links can appear or disappear at any time. The nodes in the network are equipped with hardware clocks, but the rate of the hardware clocks can vary arbitrarily within specific bounds, and the estimates that nodes can obtain about the clock values of other nodes are inherently inaccurate. Our goal in this setting is to output a logical clock at each node, such that the logical clocks of any two nodes are not too far apart, and nodes that remain close to each other in the network for a long time are better synchronized than distant nodes. This property is called gradient clock synchronization.

We present a novel clock synchronization algorithm and prove tight upper and lower bounds on the worst-case clock skew that may occur between any two participants in any given distributed system. More importantly, the worst-case clock skew between neighboring nodes is (asymptotically) at most a factor of two larger than the best possible bound. While previous results solely focused on the dependency of the skew bounds on the network diameter, we prove that our techniques are optimal also with respect to the maximum clock drift, the uncertainty in message delays, and the imposed bounds on the clock rates. The presented results all hold in a general model where both the clock drifts and the message delays may vary arbitrarily within pre-specified bounds. Furthermore, our algorithm exhibits a number of other highly desirable properties. First, the algorithm ensures that the clock values remain in an affine linear envelope of real time. A better worst-case bound on the accuracy with respect to real time cannot be achieved in the absence of an external timer. Second, the algorithm minimizes the number and size of messages that need to be exchanged in a given time period. Moreover, only a small number of bits must be stored locally for each neighbor. Finally, our algorithm can easily be adapted for a variety of other prominent synchronization models.

Data dissemination in decentralized networks is often realized by using some form of swarming technique. Swarming enables nodes to gather dynamically in order to fulfill a certain task collaboratively and to exchange resources (typically pieces of files or packets of a multimedia data stream). As in most distributed systems, swarming applications face the problem that the nodes in a network have heterogeneous capabilities or act selfishly. We investigate the problem of efficient live data dissemination (e.g., TV streams) in swarms. The live streams should be distributed in such a way that only nodes with sufficiently large contributions to the system are able to fully receive it-even in the presence of freeloading nodes or nodes that upload substantially less than required to sustain the multimedia stream. In contrast, uncooperative nodes cannot properly receive the data stream as they are unable to fill their data buffers in time, incentivizing a fair sharing of resources. If the number of selfish nodes increases, our emulation results reveal that the situation steadily deteriorates for them, while obedient nodes continue to receive virtually all packets in time.

We present a novel clock synchronization algorithm and prove tight upper and lower bounds on the worst-case clock skew that may occur between any two participants in any given distributed system. More importantly, the worst-case clock skew between neighboring nodes is (asymptotically) at most a factor of two larger than the best possible bound. While previous results solely focused on the dependency of the skew bounds on the network diameter, we prove that our techniques are optimal also with respect to the maximum clock drift, the uncertainty in message delays, and the imposed bounds on the clock rates. The presented results all hold in a general model where both the clock drifts and the message delays may vary arbitrarily within pre-specified bounds.

Over the last years, large-scale decentralized computer networks such as peer-to-peer and mobile ad hoc networks have become increasingly prevalent. The topologies of many of these networks are often highly dynamic. This is especially true for ad hoc networks formed by mobile wireless devices.

We present a distributed clock synchronization algorithm that guarantees an exponentially improved bound of O(log D) on the clock skew between neighboring nodes in any graph G of diameter D. In light of the lower bound of Omega(log D/ log log D), this result is almost tight. Moreover, the global clock skew between any two nodes, particularly nodes that are not directly connected, is bounded by O(D), which is optimal up to a constant factor. Our algorithm further ensures that the clock values are always within a linear envelope of real time. A better bound on the accuracy with respect to real time cannot be achieved in the absence of an external timer. These results all hold in a general model where both the clock drifts and the message delays may vary arbitrarily within pre-specified bounds.

This paper studies the problem of computing the most frequent element (the mode) by means of a distributed algorithm where the elements are located at the nodes of a network. Let k denote the number of distinct elements and further let mi be the number of occurrences of the element ei in the ordered list of occurrences m1m2≥ ... ≥ mk. We give a deterministic distributed algorithm with time complexity O(D+k) where D denotes the diameter of the graph, which is essentially tight. As our main contribution, a Monte Carlo algorithm is presented which computes the mode in O(D + F2/m12*log k) time with high probability, where the frequency moment Ft is defined as Ft = sumi=1k mit. This algorithm is substantially faster than the deterministic algorithm for various relevant frequency distributions. Moreover, we provide a lower bound of Omega(D+F5/(m15B)), where B is the maximum message size, that captures the effect of the frequency distribution on the time complexity to compute the mode.

This paper proposes to utilize algorithms from the probabilistic graphical models domain for Peer-to-Peer rating of data items and for computing "social influence" of nodes in a Peer-to-peer social network. We evaluate the practicality of our approach using large- scale simulations over a MSN Live Messenger subgraph consisting of about a million nodes. Our algorithms are general since they can be used for Peer-to-peer monitoring and for the efficient computation of other node ranking methods, such as PageRank and Information Centrality.

We revisit the problem of distributed k-selection where, given a general connected graph of diameter D consisting of n nodes in which each node holds a numeric element, the goal is to determine the kth smallest of these elements. In our model, there is no imposed relation between the magnitude of the stored elements and the number of nodes in the graph. We propose a randomized algorithm whose time complexity is O(DlogD n) with high probability. Additionally, a deterministic algorithm with a worst-case time complexity of O(Dlog2 D n) is presented which considerably improves the best known bound for deterministic algorithms. Moreover, we prove a lower bound of Ω(D logDn) for any randomized or deterministic algorithm, implying that the randomized algorithm is asymptotically optimal.

Peer-to-peer systems (p2p) are highly dynamic in nature. They may consist of millions of peers joining only for a limited period of time, resulting in hundreds of join and leave events per second. In this paper we introduce eQuus, a novel distributed hash table (DHT) suitable for highly dynamic environments. eQuus guarantees that lookups are always fast - in terms of both the delay and the total number of routing hops -, although peers may join and leave the network at any time and concurrently

Positioning based on received signal strengths from base stations is highly sensitive to the effects of signal attenuation, reflection, and scattering. Moreover, experimental measurements show that received signal strengths fluctuate significantly over time due to noise and interference. Unlike most of the related work our approach does not rely on the presence of a priori information about base station positions, objects influencing radio signal propagation, and signal propagation characteristics. Instead of trying to compute the current position defined by coordinates, the goal of our system is to infer a user's present logical position. In addition, our system particularly differs from previous work in the chosen statistical approach, which explicitly copes with signal strength fluctuation over time and allows control over system accuracy by means of specification of confidence intervals.