Vinay J. Ribeiro

Shubham Anand Jain, Rohan Shah, Sanit Gupta et al.

We consider the problem of correctly identifying the \textit{mode} of a discrete distribution $\mathcal{P}$ with sufficiently high probability by observing a sequence of i.i.d. samples drawn from $\mathcal{P}$. This problem reduces to the estimation of a single parameter when $\mathcal{P}$ has a support set of size $K = 2$. After noting that this special case is tackled very well by prior-posterior-ratio (PPR) martingale confidence sequences \citep{waudby-ramdas-ppr}, we propose a generalisation to mode estimation, in which $\mathcal{P}$ may take $K \geq 2$ values. To begin, we show that the "one-versus-one" principle to generalise from $K = 2$ to $K \geq 2$ classes is more efficient than the "one-versus-rest" alternative. We then prove that our resulting stopping rule, denoted PPR-1v1, is asymptotically optimal (as the mistake probability is taken to $0$). PPR-1v1 is parameter-free and computationally light, and incurs significantly fewer samples than competitors even in the non-asymptotic regime. We demonstrate its gains in two practical applications of sampling: election forecasting and verification of smart contracts in blockchains.

Aditya Ahuja, Vinay J. Ribeiro, Ranjan Pal

Permissionless blockchain consensus protocols have been designed primarily for defining decentralized economies for the commercial trade of assets, both virtual and physical, using cryptocurrencies. In most instances, the assets being traded are regulated, which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formally recognise proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in rampant illegal activities in the associated crypto-economies. In this contribution, we motivate the need for regulated blockchain consensus protocols with a case study of the illegal, cryptocurrency based, Silk Road darknet market. We present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain distributed consensus. As per our regulatory framework, we derive conditions under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that with a small change to the standard blockchain consensus execution policy (appropriately introduced through regulation), the legal transaction throughput in the blockchain network can be maximized.

Sourav Das, Nitin Awathare, Ling Ren et al.

Proof-of-Work~(PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time~($\mathbb{I}$) between blocks for validating transactions. A trivial increase in validation time~($τ$) introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and increases unfairness. Large $τ$ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust issues that are non-trivial to resolve. In this paper, we present Tuxedo, the first on-chain protocol to theoretically scale $τ/\mathbb{I} \approx 1$ in PoW blockchains. The key innovation in Tuxedo is to separate the consensus on the ordering of transactions from their execution. We achieve this by allowing miners to delay validation of transactions in a block by up to $ζ$ blocks, where $ζ$ is a system parameter. We perform security analysis of Tuxedo considering all possible adversarial strategies in a synchronous network with end-to-end delay $Δ$ and demonstrate that Tuxedo achieves security equivalent to known results for longest chain PoW Nakamoto consensus. Additionally, we also suggest a principled approach for practical choices of parameter $ζ$ as per the application requirement. Our prototype implementation of Tuxedo atop Ethereum demonstrates that it can scale $τ$ without suffering the harmful effects of naive scaling in existing blockchains.

Sourav Das, Samuel Wedaj, Kolin Paul et al.

The proliferation of application specific cyber-physical systems coupled with the emergence of a variety of attacks on such systems (malware such as Mirai and Hajime) underlines the need to secure such networks. Most existing security efforts have focused on only detection of the presence of malware. However given the ability of most attacks to spread through the network once they infect a few devices, it is important to contain the spread of a virus and at the same time systematically cleanse the impacted nodes using the communication capabilities of the network. Toward this end, we present Airmed - a method and system to not just detect corruption of the application software on a IoT node, but to self correct itself using its neighbors. Airmed's decentralized mechanisms prevent the spread of self-propagating malware and can also be used as a technique for updating application code on such IoT devices. Among the novelties of Airmed are a novel bloom-filter technique along with hardware support to identify position of the malware program from the benign application code, an adaptive self-check for computational efficiency, and a uniform random-backoff and stream signatures for secure and bandwidth efficient code exchange to correct corrupted devices. We assess the performance of Airmed, using the embedded systems security architecture of TrustLite in the OMNeT++ simulator. The results show that Airmed scales up to thousands of devices, ensures guaranteed update of the entire network, and can recover 95% of the nodes in 10 minutes in both internal and external propagation models. Moreover, we evaluate memory and communication costs and show that Airmed is efficient and incurs very low overhead.