Related papers: Free2Shard: Adaptive-adversary-resistant sharding …
Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant…
Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial…
By leveraging the principle of software polyculture to ensure security in a network, we proposed a vulnerability-based software diversity metric to determine how a network topology can be adapted to minimize security vulnerability while…
The Narwhal system is a state-of-the-art Byzantine fault-tolerant scalable architecture that involves constructing a directed acyclic graph (DAG) of messages among a set of validators in a Blockchain network. Bullshark is a zero-overhead…
Blockchain systems benefit from lessons in prior art such as fault tolerance, distributed systems, peer-to-peer systems, and game theory. In this paper we argue that blockchain algorithms should tolerate both rational (self-interested)…
Recent account allocation studies in sharded blockchains are typically miner-driven, requiring miners to perform global optimizations for all accounts to enhance system-wide performance. This forces each miner to maintain a complete copy of…
The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of spatial data in real-time. The current scale of spatial data cannot be handled using…
We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be adversarially changed to trick the algorithm,…
Despite being under development for over 15 years, transaction throughput remains one of the key challenges confronting blockchains, which typically has a cap of a limited number of transactions per second. A fundamental factor limiting…
A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We establish a connection between adversarial robustness of…
We study structural clustering on graphs in dynamic scenarios, where the graphs can be updated by arbitrary insertions or deletions of edges/vertices. The goal is to efficiently compute structural clustering results for any clustering…
Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent…
Polkadot is a network protocol launched in 2020 with the ambition of unlocking the full potential of blockchain technologies. Its novel multi-chain protocol allows arbitrary data to be transferred across heterogeneous blockchains, enabling…
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a…
We describe a simple and yet very scalable implementation of static functions (VFunc) and of static filters (VFilter) based on hypergraphs. We introduce the idea of {\epsilon}-cost sharding, which allows us to build structures that can…
Public blockchains should be able to scale with respect to the number of nodes and to the transactions workload. The blockchain scalability trilemma has been informally conjectured. This is related to scalability, security and…
Despite considerable efforts on making them robust, real-world AI-based systems remain vulnerable to decision based attacks, as definitive proofs of their operational robustness have so far proven intractable. Canonical robustness…
In this paper, we present a pricing mechanism that aligns incentives of agents who exchange resources on a decentralized ledger with the goal of maximizing transaction throughput. Subdividing a blockchain ledger into shards promises to…
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the adversarial training process, there are few investigations…
We consider streaming over a peer-to-peer network with homogeneous nodes in which a single source broadcasts a data stream to all the users in the system. Peers are allowed to enter or leave the system (adversarially) arbitrarily. Previous…