arXiv subjects

Maxwell Young

Publications and source records attributed to Maxwell Young.

At least 19 recordsLinked to original sources

arXiv2025

A Survey of Recent Advancements in Secure Peer-to-Peer Networks

Peer-to-peer (P2P) networks are a cornerstone of modern computing, and their security is an active area of research. Many defenses with strong security guarantees have been proposed; however, the most-recent survey is over a decade old. This paper delivers an updated review of recent theoretical advances that address classic threats, such as the Sybil and routing attacks, while highlighting how emerging trends -- such as machine learning, social networks, and dynamic systems -- pose new challenges and drive novel solutions. We evaluate the strengths and weaknesses of these solutions and suggest directions for future research.

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arXiv2025

A Gentle Wakeup Call: Symmetry Breaking with Less Collision Cost

The wakeup problem addresses the fundamental challenge of symmetry breaking. Initially, n devices share a time-slotted multiple access channel, which models wireless communication. A transmission succeeds if exactly one device sends in a slot; if two or more transmit, a collision occurs and none succeed. The goal is to achieve a single successful transmission efficiently. Prior work on wakeup primarily analyzes latency -- the number of slots until the first success. However, in many modern systems, each collision incurs a nontrivial delay, C, which prior analyses neglect. Consequently, although existing algorithms achieve polylogarithmic-in-n latency, they still suffer a delay of \Omega(C) due to collisions. Here, we design and analyze a randomized wakeup algorithm, Aim-High. When C is sufficiently large with respect to n, Aim-High has expected latency and expected total cost of collisions that are nearly O(\sqrt{C}); otherwise, both quantities are O(poly{\log n}). Finally, for a well-studied class of algorithms, we establish a trade-off between latency and expected total cost of collisions.

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arXiv2024

Softening the Impact of Collisions in Contention Resolution

Contention resolution addresses the problem of coordinating access to a shared communication channel. Time is discretized into synchronized slots, and a packet can be sent in any slot. If no packet is sent, then the slot is empty; if a single packet is sent, then it is successful; and when multiple packets are sent at the same time, a collision occurs, resulting in the failure of the corresponding transmissions. In each slot, every packet receives ternary channel feedback indicating whether the current slot is empty, successful, or a collision. Much of the prior work on contention resolution has focused on optimizing the makespan, which is the number of slots required for all packets to succeed. However, in many modern systems, collisions are also costly in terms of the time they incur, which existing contention-resolution algorithms do not address. In this paper, we design and analyze a randomized algorithm, Collision Aversion Backoff (CAB), that optimizes both the makespan and the collision cost. We consider the static case where an unknown $n\geq 2$ packets are initially present in the system, and each collision has a known cost $\mathcal{C}$, where $1 \leq \mathcal{C} \leq n^{\kappa}$ for a known constant $\kappa\geq 0$. With error probability polynomially small in $n$, CAB guarantees that all packets succeed with makespan and a total expected collision cost of $\tilde{O}(n\sqrt{\mathcal{C}})$. We give a lower bound for the class of fair algorithms: where, in each slot, every packet executing the fair algorithm sends with the same probability (and the probability may change from slot to slot). Our lower bound is asymptotically tight up to a $\texttt{poly}(\log n)$-factor for sufficiently large $\mathcal{C}$.

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arXiv2024

A Survey on Adversarial Contention Resolution

Contention resolution addresses the challenge of coordinating access by multiple processes to a shared resource such as memory, disk storage, or a communication channel. Originally spurred by challenges in database systems and bus networks, contention resolution has endured as an important abstraction for resource sharing, despite decades of technological change. Here, we survey the literature on resolving worst-case contention, where the number of processes and the time at which each process may start seeking access to the resource is dictated by an adversary. We also highlight the evolution of contention resolution, where new concerns -- such as security, quality of service, and energy efficiency -- are motivated by modern systems. These efforts have yielded insights into the limits of randomized and deterministic approaches, as well as the impact of different model assumptions such as global clock synchronization, knowledge of the number of processors, feedback from access attempts, and attacks on the availability of the shared resource.

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arXiv2023

Defending Hash Tables from Subterfuge with Depth Charge

We consider the problem of defending a hash table against a Byzantine attacker that is trying to degrade the performance of query, insertion and deletion operations. Our defense makes use of resource burning (RB) -- the the verifiable expenditure of network resources -- where the issuer of a request incurs some RB cost. Our algorithm, Depth Charge, charges RB costs for operations based on the depth of the appropriate object in the list that the object hashes to in the table. By appropriately setting the RB costs, our algorithm mitigates the impact of an attacker on the hash table's performance. In particular, in the presence of a significant attack, our algorithm incurs a cost which is asymptotically less that the attacker's cost.

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arXiv2023

Fully Energy-Efficient Randomized Backoff: Slow Feedback Loops Yield Fast Contention Resolution

Contention resolution addresses the problem of coordinating access to a shared channel. Time proceeds in slots, and a packet transmission can be made in any slot. A packet is successfully sent if no other packet is also transmitted during that slot. If two or more packets are sent in the same slot, then none of these transmissions succeed. Listening during a slot gives ternary feedback, indicating if that slot had (0) silence, (1) a successful transmission, or (2+) noise. No other feedback is available. Packets are (adversarially) injected into the system over time. A packet departs the system once it is successful. The goal is to send all packets while optimizing throughput, which is roughly the fraction of successful slots. Most prior algorithms with constant throughput require a short feedback loop, in the sense that a packet's sending probability in slot t+1 is fully determined by its internal state at slot t and the channel feedback at slot t. An open question is whether these short feedback loops are necessary; that is, how often must listening and updating occur in order to achieve constant throughput? This question addresses energy efficiency, since both listening and sending consume significant energy. The channel can also suffer adversarial noise ("jamming"), which causes any listener to hear noise, even when no packets are sent. How does jamming affect our goal of long feedback loops/energy efficiency? Connecting these questions, we ask: what does a contention-resolution algorithm have to sacrifice to reduce channel accesses? Must we give up on constant throughput or robustness to noise? Here, we show that we need not concede anything. Suppose there are N packets and J jammed slots, where the input is determined by an adaptive adversary. We give an algorithm that, with high probability in N+J, has constant throughput and polylog(N+J) channel accesses per packet.

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arXiv2022

Bankrupting DoS Attackers

Can we make a denial-of-service attacker pay more than the server and honest clients? Consider a model where a server sees a stream of jobs sent by either honest clients or an adversary. The server sets a price for servicing each job with the aid of an estimator, which provides approximate statistical information about the distribution of previously occurring good jobs. We describe and analyze pricing algorithms for the server under different models of synchrony, with total cost parameterized by the accuracy of the estimator. Given a reasonably accurate estimator, the algorithm's cost provably grows more slowly than the attacker's cost, as the attacker's cost grows large. Additionally, we prove a lower bound, showing that our pricing algorithm yields asymptotically tight results when the estimator is accurate within constant factors.

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arXiv2022

A Policy Driven AI-Assisted PoW Framework

Proof of Work (PoW) based cyberdefense systems require incoming network requests to expend effort solving an arbitrary mathematical puzzle. Current state of the art is unable to differentiate between trustworthy and untrustworthy connections, requiring all to solve complex puzzles. In this paper, we introduce an Artificial Intelligence (AI)-assisted PoW framework that utilizes IP traffic based features to inform an adaptive issuer which can then generate puzzles with varying hardness. The modular framework uses these capabilities to ensure that untrustworthy clients solve harder puzzles thereby incurring longer latency than authentic requests to receive a response from the server. Our preliminary findings reveal our approach effectively throttles untrustworthy traffic.

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arXiv2020

Windowed Backoff Algorithms for WiFi: Theory and Performance under Batched Arrivals

Binary exponential backoff (BEB) is a decades-old algorithm for coordinating access to a shared channel. In modern networks, BEB plays an important role in WiFi (IEEE 802.11) and other wireless communication standards. Despite this track record, well-known theoretical results indicate that under bursty traffic BEB yields poor makespan, and superior algorithms are possible. To date, the degree to which these findings impact performance in wireless networks has not been examined. To address this issue, we investigate one of the strongest cases against BEB: a single burst batch of packets that simultaneously contend for access to a wireless channel. Using Network Simulator 3, we incorporate into IEEE 802.11g several newer algorithms that, while inspired by BEB, possess makespan guarantees that are theoretically superior. Surprisingly, we discover that these newer algorithms underperform BEB. Investigating further, we identify as the culprit a common abstraction regarding the cost of collisions. Our experimental results are complemented by analytical arguments that the number of collisions - and not solely makespan - is an important metric to optimize. We argue that these findings have implications for the design of backoff algorithms in wireless networks.

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arXiv2020

Bankrupting Sybil Despite Churn

A Sybil attack occurs when an adversary controls multiple identifiers (IDs) in a system. Limiting the number of Sybil (bad) IDs to a minority is critical to the use of well-established tools for tolerating malicious behavior, such as Byzantine agreement and secure multiparty computation. A popular technique for enforcing a Sybil minority is resource burning: the verifiable consumption of a network resource, such as computational power, bandwidth, or memory. Unfortunately, typical defenses based on resource burning require non-Sybil (good) IDs to consume at least as many resources as the adversary. Additionally, they have a high resource burning cost, even when the system membership is relatively stable. Here, we present a new Sybil defense, ERGO, that guarantees (1) there is always a minority of bad IDs; and (2) when the system is under significant attack, the good IDs consume asymptotically less resources than the bad. In particular, for churn rate that can vary exponentially, the resource burning rate for good IDs under ERGO is O(\sqrt{TJ} + J), where T is the resource burning rate of the adversary, and J is the join rate of good IDs. We show this resource burning rate is asymptotically optimal for a large class of algorithms. We empirically evaluate ERGO alongside prior Sybil defenses. Additionally, we show that ERGO can be combined with machine learning techniques for classifying Sybil IDs, while preserving its theoretical guarantees. Based on our experiments comparing ERGO with two previous Sybil defenses, ERGO improves on the amount of resource burning relative to the adversary by up to 2 orders of magnitude without machine learning, and up to 3 orders of magnitude using machine learning.

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arXiv2020

Resource Burning for Permissionless Systems

Proof-of-work puzzles and CAPTCHAS consume enormous amounts of energy and time. These techniques are examples of resource burning: verifiable consumption of resources solely to convey information. Can these costs be eliminated? It seems unlikely since resource burning shares similarities with "money burning" and "costly signaling", which are foundational to game theory, biology, and economics. Can these costs be reduced? Yes, research shows we can significantly lower the asymptotic costs of resource burning in many different settings. In this paper, we survey the literature on resource burning; take positions based on predictions of how the tool is likely to evolve; and propose several open problems targeted at the theoretical distributed-computing research community.

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arXiv2020

ToGCom: An Asymmetric Sybil Defense

Proof-of-work (PoW) is one of the most common techniques to defend against Sybil attacks. Unfortunately, current PoW defenses have two main drawbacks. First, they require work to be done even in the absence of an attack. Second, during an attack, they require good identities (IDs) to spend as much as the attacker. Recent theoretical work by Gupta, Saia, and Young suggests the possibility of overcoming these two drawbacks. In particular, they describe a new algorithm, GMCom, that always ensures that a minority of IDs are Sybil. They show that rate at which all good IDs perform computation is $O(J_G + \sqrt{T(J_G+1)})$, where $J_G$ is the join rate of good IDs, and $T$ is the rate at which the adversary performs computation. Unfortunately, this cost bound only holds in the case where (1) GMCom always knows the join rate of good IDs; and (2) there is a fixed constant amount of time that separates join events by good IDs. Here, we present ToGCom, which removes these two shortcomings. To do so, we design and analyze a mechanism for estimating the join rate of good IDs; and also devise a new method for setting the computational cost to join the system. Additionally, we evaluate the performance of ToGCom alongside prior PoW-based defenses. Based on our experiments, we design heuristics that further improve the performance of ToGCom by up to $3$ orders of magnitude over these previous Sybil defenses.

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arXiv2019

Resource-Competitive Sybil Defenses

Proof-of-work(PoW) is an algorithmic tool used to secure networks by imposing a computational cost on participating devices. Unfortunately, traditional PoW schemes require that correct devices perform significant computational work in perpetuity, even when the system is not under attack. We address this issue by designing general PoW protocols that ensure two properties. First, the fraction of identities in the system that are controlled by an attacker is a minority. Second, the computational cost of our protocol is comparable to the cost of an attacker. In particular, we present an efficient algorithm, GMCOM, which guarantees that the average computational cost to the good ID per unit time is O(J + sqrt(T(J+1))), where J is the average number of joins by the good IDs and T is the average computational spending of the adversary. Additionally, we discuss a precursor to this algorithm, CCOM, which guarantees an average computational cost to good IDs per unit time of O(J+T). We prove a lower bound showing that GMCOM's spending rate is asymptotically optimal among a large family of algorithms. Finally, we provide empirical evidence that our algorithms can be significantly more efficient than previous defenses under various attack scenarios.

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arXiv2019

Singletons for Simpletons: Revisiting Windowed Backoff using Chernoff Bounds

Backoff algorithms are used in many distributed systems where multiple devices contend for a shared resource. For the classic balls-into-bins problem, the number of singletons -- those bins with a single ball -- is important to the analysis of several backoff algorithms; however, existing analyses employ advanced probabilistic tools to obtain concentration bounds. Here, we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing some well-known backoff algorithms.

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arXiv2018

Proof of Work Without All the Work: Computationally Efficient Attack-Resistant Systems

Proof-of-work (PoW) is an algorithmic tool used to secure networks by imposing a computational cost on participating devices. Unfortunately, traditional PoW schemes require that correct devices perform computational work perpetually, even when the system is not under attack. We address this issue by designing a general PoW protocol that ensures two properties. First, the network stays secure. In particular, the fraction of identities in the system that are controlled by an attacker is always less than 1/2. Second, our protocol's computational cost is commensurate with the cost of an attacker. In particular, the total computational cost of correct devices is a linear function of the attacker's computational cost plus the number of correct devices that have joined the system. Consequently, if the network is attacked, we ensure security with cost that grows linearly with the attacker's cost; and, in the absence of attack, our computational cost remains small. We prove similar guarantees for bandwidth cost. Our results hold in a dynamic, decentralized system where participants join and depart over time, and where the total computational power of the attacker is up to a constant fraction of the total computational power of correct devices. We demonstrate how to leverage our results to address important security problems in distributed computing including: Sybil attacks, Byzantine consensus, and Committee election.

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arXiv2018

Tiny Groups Tackle Byzantine Adversaries

A popular technique for tolerating malicious faults in open distributed systems is to establish small groups of participants, each of which has a non-faulty majority. These groups are used as building blocks to design attack-resistant algorithms. Despite over a decade of active research, current constructions require group sizes of $O(\log n)$, where $n$ is the number of participants in the system. This group size is important since communication and state costs scale polynomially with this parameter. Given the stubbornness of this logarithmic barrier, a natural question is whether better bounds are possible. Here, we consider an attacker that controls a constant fraction of the total computational resources in the system. By leveraging proof-of-work (PoW), we demonstrate how to reduce the group size exponentially to $O(\log\log n)$ while maintaining strong security guarantees. This reduction in group size yields a significant improvement in communication and state costs.

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arXiv2017

Is Our Model for Contention Resolution Wrong?

Randomized binary exponential backoff (BEB) is a popular algorithm for coordinating access to a shared channel. With an operational history exceeding four decades, BEB is currently an important component of several wireless standards. Despite this track record, prior theoretical results indicate that under bursty traffic (1) BEB yields poor makespan and (2) superior algorithms are possible. To date, the degree to which these findings manifest in practice has not been resolved. To address this issue, we examine one of the strongest cases against BEB: $n$ packets that simultaneously begin contending for the wireless channel. Using Network Simulator 3, we compare against more recent algorithms that are inspired by BEB, but whose makespan guarantees are superior. Surprisingly, we discover that these newer algorithms significantly underperform. Through further investigation, we identify as the culprit a flawed but common abstraction regarding the cost of collisions. Our experimental results are complemented by analytical arguments that the number of collisions -- and not solely makespan -- is an important metric to optimize. We believe that these findings have implications for the design of contention-resolution algorithms.

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arXiv2017

A Resource-Competitive Jamming Defense

Consider a scenario where Alice wishes to send a message $m$ to Bob in a time-slotted wireless network. However, there exists an adversary, Carol, who aims to prevent the transmission of $m$ by jamming the communication channel. There is a per-slot cost of $1$ to send, receive or jam $m$ on the channel, and we are interested in how much Alice and Bob need to spend relative to Carol in order to guarantee communication. Our approach is to design an algorithm in the framework of resource-competitive analysis where the cost to correct network devices (i.e., Alice and Bob) is parameterized by the cost to faulty devices (i.e., Carol). We present an algorithm that guarantees the successful transmission of $m$ and has the following property: if Carol incurs a cost of $T$ to jam, then both Alice and Bob have a cost of $O(T^{φ- 1} + 1)=O(T^{.62}+1)$ in expectation, where $φ= (1+ \sqrt{5})/2$ is the golden ratio. In other words, it possible for Alice and Bob to communicate while incurring asymptotically less cost than Carol. We generalize to the case where Alice wishes to send $m$ to $n$ receivers, and we achieve a similar result. Our findings hold even if (1) $T$ is unknown to either party; (2) Carol knows the algorithms of both parties, but not their random bits; (3) Carol can jam using knowledge of past actions of both parties; and (4) Carol can jam reactively, so long as there is sufficient network traffic in addition to $m$.

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