Vinayaka Pandit

Ermyas Abebe, Yining Hu, Allison Irvin et al.

Permissioned ledger technologies have gained significant traction over the last few years. For practical reasons, their applications have focused on transforming narrowly scoped use-cases in isolation. This has led to a proliferation of niche, isolated networks that are quickly becoming data and value silos. To increase value across the broader ecosystem, these networks must seamlessly integrate with existing systems and interoperate with one another. A fundamental requirement for enabling crosschain communication is the ability to prove the validity of the internal state of a ledger to an external party. However, due to the closed nature of permissioned ledgers, their internal state is opaque to an external observer. This makes consuming and verifying states from these networks a non-trivial problem. This paper addresses this fundamental requirement for state sharing across permissioned ledgers. In particular, we address two key problems for external clients: (i) assurances on the validity of state in a permissioned ledger and (ii) the ability to reason about the currency of state. We assume an adversarial model where the members of the committee managing the permissioned ledger can be malicious in the absence of detectability and accountability. We present a formalization of the problem for state sharing and examine its security properties under different adversarial conditions. We propose the design of a protocol that uses a secure public ledger for providing guarantees on safety and the ability to reason about time, with at least one honest member in the committee. We then provide a formal security analysis of our design and a proof of concept implementation based on Hyperledger Fabric demonstrating the effectiveness of the proposed protocol.

Sneha Chaudhari, Pankaj Dayama, Vinayaka Pandit et al.

In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number of queries is limited by an overall budget. Arising in the context of two rather disparate applications- banking and medical diagnosis, we pose the active information acquisition problem as a constrained optimization problem. We propose two greedy iterative algorithms for solving the above problem. We conduct experiments with synthetic data and compare results of our proposed algorithms with few other baseline approaches. The experimental results show that our proposed approaches perform better than the baseline schemes.