Aayush Jain

Shreya Bhatt, Aayush Jain, Parv Maheshwari et al.

The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original paper proposes the Neural A* planner, and claims it achieves an optimal balance between the reduction of node expansions and path accuracy. We verify this claim by reimplementing the model in a different framework and reproduce the data published in the original paper. We have also provided a code-flow diagram to aid comprehension of the code structure. As extensions to the original paper, we explore the effects of (1) generalizing the model by training it on a shuffled dataset, (2) introducing dropout, (3) implementing empirically chosen hyperparameters as trainable parameters in the model, (4) altering the network model to Generative Adversarial Networks (GANs) to introduce stochasticity, (5) modifying the encoder from Unet to Unet++, (6) incorporating cost maps obtained from the Neural A* module in other variations of A* search.

Ying Feng, Aayush Jain, David P. Woodruff

Recently, the question of adversarially robust streaming, where the stream is allowed to depend on the randomness of the streaming algorithm, has gained a lot of attention. In this work, we consider a strong white-box adversarial model (Ajtai et al. PODS 2022), in which the adversary has access to all past random coins and the parameters used by the streaming algorithm. We focus on the sparse recovery problem and extend our result to other tasks such as distinct element estimation and low-rank approximation of matrices and tensors. The main drawback of previous work is that it requires a random oracle, which is especially problematic in the streaming model since the amount of randomness is counted in the space complexity of a streaming algorithm. Also, the previous work suffers from large update time. We construct a near-optimal solution for the sparse recovery problem in white-box adversarial streams, based on the subexponentially secure Learning with Errors assumption. Importantly, our solution does not require a random oracle and has a polylogarithmic per item processing time. We also give results in a related white-box adversarially robust distributed model. Our constructions are based on homomorphic encryption schemes satisfying very mild structural properties that are currently satisfied by most known schemes.

Aayush Jain, Varun Kohli, Girish Mishra

Recent years have seen an increasing involvement of Deep Learning in the cryptanalysis of various ciphers. The present study is inspired by past works on differential distinguishers, to develop a Deep Neural Network-based differential distinguisher for round reduced lightweight block ciphers PRESENT and Simeck. We make improvements in the state-of-the-art approach and extend its use to the two structurally different block ciphers, PRESENT-80 and Simeck64/128. The obtained results suggest the universality of our cryptanalysis method. The proposed method can distinguish random data from the cipher data obtained until 6 rounds of PRESENT and 7 rounds of Simeck encryption with high accuracy. In addition to this, we explore a new approach to select good input differentials, which to the best of our knowledge has not been explored in the past. We also provide a minimum-security requirement for the discussed ciphers against our differential attack.

Aayush Jain, Huijia Lin, Amit Sahai

In this work, we show how to construct indistinguishability obfuscation from subexponential hardness of four well-founded assumptions. We prove: Let $τ\in (0,\infty), δ\in (0,1), ε\in (0,1)$ be arbitrary constants. Assume sub-exponential security of the following assumptions, where $λ$ is a security parameter, and the parameters $\ell,k,n$ below are large enough polynomials in $λ$: - The SXDH assumption on asymmetric bilinear groups of a prime order $p = O(2^λ)$, - The LWE assumption over $\mathbb{Z}_{p}$ with subexponential modulus-to-noise ratio $2^{k^ε}$, where $k$ is the dimension of the LWE secret, - The LPN assumption over $\mathbb{Z}_p$ with polynomially many LPN samples and error rate $1/\ell^δ$, where $\ell$ is the dimension of the LPN secret, - The existence of a Boolean PRG in $\mathsf{NC}^0$ with stretch $n^{1+τ}$, Then, (subexponentially secure) indistinguishability obfuscation for all polynomial-size circuits exists.