We present a fully homomorphic encryption scheme which natively supports arithmetic and logical operations over large “machine words”, namely plaintexts of the form ℤ_2^n (e.g. n=64 ). Our scheme builds on the well-known BGV framework, but deviates in the selection of number field and in the encoding of messages. This allows us to support large message spaces with only modest effect on the noise growth. Arithmetic operations (modulo 2^n ) are supported natively similarly to BGV-style FHE schemes, and we present an efficient bootstrapping procedure for our scheme. Our bootstrapping algorithm has the feature that along the way it decomposes our machine word into bits, so that during bootstrapping it is possible to perform logical operations (essentially addressing each bit in the message independently). This means that during a single bootstrapping cycle we can perform logical operations on n bits. For example, a “greater than” operation (if x> y output 1, otherwise 0), only requires a single subtraction and a single bootstrapping cycle. Along the way we present a number of new tools and techniques, such as a generalization of the BGV modulus switching technique to a setting where the plaintext and ciphertext moduli are ideals (and not numbers).

Multi-robot graph exploration is a central problem in robotics, planning, and multi-agent systems. In this work, we consider the problem of exploring an unknown $n$-node graph by $k$ robots that must remain connected throughout the process. Such a connectivity is frequently required for safety reasons, and naturally arises in real-world applications such as search-and-rescue and maintenance operations. We study the \emph{overhead} imposed by not knowing the graph in advance, measured in terms of the \emph{competitive ratio} of the number of exploration rounds necessary when the graph is unknown (versus the case that it is known). We introduce a novel exploration procedure, \textsf{DFS-BGS}, to tackle the problem, and analyze its performance both theoretically and experimentally. On the theoretical end, \textsf{DFS-BGS} provably achieves a competitive ratio $\tilde{\mathcal{O}}(k^{1/3})$, for the case $n\leq k$. Empirically, we compare our online $\textsf{DFS-BGS}$ to $\textsf{COCTA}$~\cite{sinay2017maintaining}, the SOTA algorithm for trees that are known in advance. Examining the performance of the algorithms on real-world hotel floor plans as well as random graphs over a wide range of parameters, $\textsf{DFS-BGS}$ incurs only a small slowdown, even with hundreds of robots and thousands of nodes.

Threshold signatures are a fundamental primitive in applied cryptography, primarily used to mitigate the custodial risk involved in managing keys. However, existing constructions rely on synchronous communication assumptions and fixed participant sets, limiting their applicability to real-world networks. In addition, as the committees managing those keys serve an ever-growing number of clients and assets, they become lucrative targets for attacks. This issue is often called the Honeypot Problem. A recent work proposed the 2PC-MPC paradigm, in which a client and a decentralized network jointly generate signatures, aiming to mitigate this issue. In this work, we present the first asynchronous 2PC-MPC protocol for ECDSA signatures, designed to operate over reliable broadcast channels as implemented in modern blockchains. Our protocol tolerates dynamic, post-determined quorums whose participants may change between rounds. This allows the protocol to align with asynchronous consensus layers while providing identifiable abort, public verifiability, guaranteed output delivery (assuming completeness of the broadcast channel) and censorship resistance for the client. We introduce global presigns, which are generated autonomously by the network and remain client-agnostic until signing. This enables effective background preprocessing, and results in sub-second online signing network-side latency for dozens of concurrent clients. From a technical perspective, our asynchronous design eliminates commitment rounds, yielding one-round client interaction for both key generation and signing. We also remove the need for zero-knowledge proofs toward the client. This results in a client complexity independent of network size, without proof aggregation (which is particularly challenging over asynchronous channels). Our constructions are proven UC-secure in the asynchronous setting. To achieve concrete security over 256-bit elliptic curves, we introduce the Slightly-Enhanced ECDSA Unforgeability assumption and provide a tight reduction in the EC-GGM model. Beyond theoretical analysis, we implement our protocols and evaluate them in both a local benchmark and a live permissionless deployment with dozens of validators, demonstrating practical performance and robustness under heterogeneous execution speeds.

To counter an imminent multi-drone attack on a city, defenders have deployed drones across the city. These drones must intercept/eliminate the threat, thus reducing potential damage from the attack. We model this as a Sequential Stackelberg Security Game, where the defender first commits to a mixed sequential defense strategy, and the attacker then best responds. We develop an efficient algorithm called S2D2, which outputs a defense strategy. We demonstrate the efficacy of S2D2 in extensive experiments on data from 80 real cities, improving the performance of the defender in comparison to greedy heuristics based on prior works. We prove that under some reasonable assumptions about the city structure, S2D2 outputs an approximate Strong Stackelberg Equilibrium (SSE) with a convenient structure.

Multi-Robot Coverage problems have been extensively studied in robotics, planning and multi-agent systems. In this work, we consider the coverage problem when there are constraints on the proximity (e.g., maximum distance between the agents, or a blue agent must be adjacent to a red agent) and the movement (e.g., terrain traversability and material load capacity) of the robots. Such constraints naturally arise in many real-world applications, e.g. in search-and-rescue and maintenance operations. Given such a setting, the goal is to compute a covering tour of the graph with a minimum number of steps, and that adheres to the proximity and movement constraints. For this problem, our contributions are four: (i) a formal formulation of the problem, (ii) an exact algorithm that is FPT in parameters ||F||, d and ω - the set of robot formations that encode the proximity constraints, the maximum nodes degree, and the tree-width of the graph, respectively, (iii) for the case that the graph is a tree: a PTAS approximation scheme, that given an ε produces a tour that is within a 1+ ε⋅error(||F||, d)) of the optimal one, and the computation runs in time poly(n) ⋅ h(1/ε, ||F||). (iv) for the case that the graph is a tree, with k=3 robots, and the constraint is that all agents are connected: a PTAS scheme with multiplicative approximation error of 1 + O(ε), independent of d.

Adversarial environments require agents to navigate a key strategic trade-off: acquiring information enhances situational awareness, but may simultaneously expose them to threats. To investigate this tension, we formulate a PursuitEvasion-Exposure-Concealment Game (PEEC) in which a pursuer agent must decide when to communicate in order to obtain the evader's position. Each communication reveals the pursuer's location, increasing the risk of being targeted. Both agents learn their movement policies via reinforcement learning, while the pursuer additionally learns a communication policy that balances observability and risk. We propose SHADOW (Strategic-communication Hybrid Action Decision-making under partial Observation for Warfare), a multi-headed sequential reinforcement learning framework that integrates continuous navigation control, discrete communication actions, and opponent modeling for behavior prediction. Empirical evaluations show that SHADOW pursuers achieve higher success rates than six competitive baselines. Our ablation study confirms that temporal sequence modeling and opponent modeling are critical for effective decision-making. Finally, our sensitivity analysis reveals that the learned policies generalize well across varying communication risks and physical asymmetries between agents.

In the threshold version of Paillier’s encryption scheme, a set of parties collectively holds the secret decryption key through a secret sharing scheme. Whenever a ciphertext is to be decrypted, the parties send their decryption shares, which are then verified for correctness and combined into the plaintext. The scheme has been widely adopted in various applications, from secure voting to general purpose MPC protocols. However, among the handful of existing proposals for a maliciously secure scheme, one must choose between an efficient implementation that relies on non-standard assumptions or a computationally expensive implementation that relies on widely acceptable assumptions. In this work, we show that one can enjoy the benefits of both worlds. Specifically, we adjust a scheme by Damgård et al. (Int. J. Inf. Secur. 2010) to get a practical distributed key generation (DKG). While the original scheme was only known to be secure under ad-hoc non-standard assumptions, we prove that the adjusted scheme is in fact secure under the decisional composite residuosity (DCR) assumption alone, required for the semantic security of the Pallier encryption scheme itself. This is possible thanks to a novel reduction technique, from computing and proving a false decryption share, to the factoring problem. Specifically, while there may exist false decryption shares for which the zk-proof verifies with non-negligible probability, they are computationally hard to find. Furthermore, we use similar ideas to prove that batching techniques by Aditya et al. (ACNS 2004), which allows a prover to batch several statements into a single proof, can be applied to our adjusted scheme. This enables a batched threshold Paillier decryption in the fully distributed setting for the first time. Until now, verifying that a decryption share is correct was the bottleneck of threshold Paillier schemes and hindered real world deployments (unless one is willing to rely on a trusted dealer). Our work accumulates to shifting the bottleneck back to the plaintext reconstruction, just like in the semi-honest setting, and renders threshold Paillier practical for the first time, supporting large scale deployments. We exemplify this shift by implementing the scheme and report our evaluation with up to 1000 parties, in the dishonest majority setting. Over an EC2 c6i machine, we get a throughput of about 50 and 3.6 decryptions per second, when run over a network of 100 and 1000 parties, respectively.

Fair allocation of indivisible goods has been studied extensively. However, the solutions offered to date are not resilient to subsequent changes that may occur after the allocation has been decided and executed, e.g., agents leaving the system, or additional goods are discovered. Currently, such settings require rerunning the allocation algorithm from scratch, potentially shifting most allocated goods between the agents. This can be cumbersome at best, or impossible at worst. In this paper, we study the notion of resilience, which quantifies the number of changes needed to resolve subsequent changes in the environment. We then apply it to the problem of fair allocation of indivisible goods, focusing on the EF1 and EFX solution concepts. For the EF1 solution concept, we provide constructive and efficient algorithms to restore EF1 after a simultaneous loss of goods, addition of new goods, and resignation of agents. We show that the addition of new agents cannot be resolved efficiently when the agents' valuation may be arbitrary. When agents have identical valuations, we show how to accept new agents efficiently. For the EFX solution concept, we (mostly) prove negative results, establishing that restoring EFX may be prohibitively costly, even for agents with identical valuations.

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature. However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium. In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate (alpha) core, and provide an efficient construction thereof. Lastly, we show that stronger types of the core may be empty. Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.

We study Stackelberg security game (SSG) with multiple defenders, where heterogeneous defenders need to allocate security resources to protect a set of targets against a strategic attacker. In such games, coordination and cooperation between the defenders can increase their ability to protect their assets, but the heterogeneous preferences of the selfinterested defenders often make such cooperation very difficult. In this paper, we approach the problem from the perspective of cooperative game theory and study coalition formation among the defenders. Our main contribution is a number of algorithmic results for the computation problems that arise in this model. We provide a poly-time algorithm for computing a solution in the core of the game and show that all of the elements in the core are Pareto efficient. We show that the problem of computing the entire core is NP-hard and then delve into a special setting where the size of a coalition is limited up to some threshold. We analyse the parameterized complexity of deciding if a coalition structure is in the core under this special setting, and provide a poly-time algorithm for computing successful deviation strategies for a given coalition.