arXiv subjects

Bobby Bhattacharjee

Publications and source records attributed to Bobby Bhattacharjee.

7 recordsLinked to original sources

arXiv2026

Rate-Fidelity Control for Wide-Area Quantum Links

Quantum network links must distribute entanglement at high rates while satisfying application-specified fidelity demands. However, wide-area deployed fiber links suffer from polarization drift which destabilizes end-to-end fidelity and forces periodic compensation. Current deployments often use active stabilization with fixed control policies, and improvements generally stem from advances in quantum hardware. Meanwhile, software control remains relatively underexplored. Here, we formulate quantum link operation as a joint control problem over tunable rate-fidelity tradeoffs and uncontrollable link drift. From this framework, we construct a link control protocol that dynamically adapts source pump power and polarization compensation to maximize entanglement distribution rate subject to a minimum fidelity constraint. We evaluate the protocol through trace-driven simulations driven by data from a 64 km deployed optical fiber. Compared with optimized static policies, our adaptive controller improves mean entanglement distribution rate by 14% over a 24 hour trace, without requiring any offline policy optimization. Our results show that software-based physical layer control can provide a practical mechanism for improving near-term quantum link performance without requiring additional quantum hardware.

quant-ph↗

arXiv2024

Efficient Routing on Quantum Networks using Adaptive Clustering

We introduce QuARC, Quantum Adaptive Routing using Clusters, a novel clustering-based entanglement routing protocol that leverages redundant, multi-path routing through multi-particle projective quantum measurements to enable high-throughput, low-overhead, starvation-free entanglement distribution. At its core, QuARC periodically reconfigures the underlying quantum network into clusters of different sizes, where each cluster acts as a small network that distributes entanglement across itself, and the end-to-end entanglement is established by further distributing between clusters. QuARC does not require a-priori knowledge of any physical parameters, and is able to adapt the network configuration using static topology information, and using local (within-cluster) measurements only. We present a comprehensive simulation-based evaluation that shows QuARC is robust against changes to physical network parameters, and maintains high throughput without starvation even as network sizes scale and physical parameters degrade.

quant-ph↗

arXiv2022

CoVault: A Secure Analytics Platform

Analytics on personal data, such as individuals' mobility, financial, and health data can be of significant benefit to society. Such data is already collected by smartphones, apps and services today, but liberal societies have so far refrained from making it available for large-scale analytics. Arguably, this is due at least in part to the lack of an analytics platform that can secure data through transparent, technical means (ideally with decentralized trust), enforce source policies, handle millions of distinct data sources, and run queries on billions of records with acceptable query latencies. To bridge this gap, we present an analytics platform called CoVault which combines secure multi-party computation (MPC) with trusted execution environment (TEE)-based delegation of trust to be able execute approved queries on encrypted data contributed by individuals within a datacenter to achieve the above properties. We show that CoVault scales well despite the high cost of MPC. For example, CoVault can process data relevant to epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using a core pair for every 20,000 people. Compared to a state-of-the-art MPC-based platform, CoVault can process queries between 7 to over 100 times faster, as well as scale to many sources and big data.

cs.CR↗

arXiv2020

SeCloak: ARM Trustzone-based Mobile Peripheral Control

Reliable on-off control of peripherals on smart devices is a key to security and privacy in many scenarios. Journalists want to reliably turn off radios to protect their sources during investigative reporting. Users wish to ensure cameras and microphones are reliably off during private meetings. In this paper, we present SeCloak, an ARM TrustZone-based solution that ensures reliable on-off control of peripherals even when the platform software is compromised. We design a secure kernel that co-exists with software running on mobile devices (e.g., Android and Linux) without requiring any code modifications. An Android prototype demonstrates that mobile peripherals like radios, cameras, and microphones can be controlled reliably with a very small trusted computing base and with minimal performance overhead.

cs.CR↗

arXiv2019

Finding Safety in Numbers with Secure Allegation Escrows

For fear of retribution, the victim of a crime may be willing to report it only if other victims of the same perpetrator also step forward. Common examples include 1) identifying oneself as the victim of sexual harassment, especially by a person in a position of authority or 2) accusing an influential politician, an authoritarian government, or ones own employer of corruption. To handle such situations, legal literature has proposed the concept of an allegation escrow: a neutral third-party that collects allegations anonymously, matches them against each other, and de-anonymizes allegers only after de-anonymity thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached. An allegation escrow can be realized as a single trusted third party; however, this party must be trusted to keep the identity of the alleger and content of the allegation private. To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced "say"). A SAE is a group of parties with independent interests and motives, acting jointly as an escrow for collecting allegations from individuals, matching the allegations, and de-anonymizing the allegations when designated thresholds are reached. By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers). Once a sufficient number of matching allegations exist, the join escrow discloses the allegation with the allegers' identities. We describe how SAEs can be constructed using a novel authentication protocol and a novel allegation matching and bucketing algorithm, provide formal proofs of the security of our constructions, and evaluate a prototype implementation, demonstrating feasibility in practice.

cs.CR↗

arXiv2018

TxProbe: Discovering Bitcoin's Network Topology Using Orphan Transactions

Bitcoin relies on a peer-to-peer overlay network to broadcast transactions and blocks. From the viewpoint of network measurement, we would like to observe this topology so we can characterize its performance, fairness and robustness. However, this is difficult because Bitcoin is deliberately designed to hide its topology from onlookers. Knowledge of the topology is not in itself a vulnerability, although it could conceivably help an attacker performing targeted eclipse attacks or to deanonymize transaction senders. In this paper we present TxProbe, a novel technique for reconstructing the Bitcoin network topology. TxProbe makes use of peculiarities in how Bitcoin processes out of order, or "orphaned" transactions. We conducted experiments on Bitcoin testnet that suggest our technique reconstructs topology with precision and recall surpassing 90%. We also used TxProbe to take a snapshot of the Bitcoin testnet in just a few hours. TxProbe may be useful for future measurement campaigns of Bitcoin or other cryptocurrency networks.

cs.CR↗

arXiv2018

Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression

We propose tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order ones. The problem of parameter learning is challenging, as it corresponds to hierarchical nonlinear tensor decomposition. We propose to solve the learning problem using stochastic gradient descent by deriving nontrivial backpropagation rules in generalized tensor algebra we introduce. Our proposed TNNs has three advantages over existing neural networks: (1) TNNs naturally apply to high order input object and thus preserve the multi-dimensional structure in the input, as there is no need to flatten the data. (2) TNNs interpret designs of existing neural network architectures. (3) Mapping a neural network to TNNs with the same expressive power results in a TNN of fewer parameters. TNN based compression of neural network improves existing low-rank approximation based compression methods as TNNs exploit two other types of invariant structures, periodicity and modulation, in addition to the low rankness. Experiments on LeNet-5 (MNIST), ResNet-32 (CIFAR10) and ResNet-50 (ImageNet) demonstrate that our TNN based compression outperforms (5% test accuracy improvement universally on CIFAR10) the state-of-the-art low-rank approximation based compression methods under the same compression rate, besides achieving orders of magnitude faster convergence rates due to the efficiency of TNNs.

stat.ML↗