|| Commitment Capacity under Cost Constraints
||Manideep Mamindlapally, Indian Institute of Technology Kharagpur, India; Anuj Kumar Yadav, Indian Institute of Technology Patna, India; Manoj Mishra, National Institute of Science Education and Research, Bhubaneshwar Homi Bhabha National Institute, India; Amitalok Jayant Budkuley, Indian Institute of Technology Kharagpur, India|
||D7-S4-T4: Topics in Privacy & Cryptography II
||Tuesday, 20 July, 23:00 - 23:20
||Tuesday, 20 July, 23:20 - 23:40
We study the problem of commitment over channels under cost constraints. Commitment is a widely studied cryptographic primitive, where two mutually distrustful parties, say Alice and Bob, interact over two phases of a protocol, viz., commit phase followed by reveal phase, to achieve commitment on a bit string available to Alice. Commitment (over the string) is said to occur if (i) Alice commits to the string which remains securely hidden from Bob at the end of the commit phase involving Alice’s transmission to Bob, and (ii) Alice reveals a string to Bob and Bob is able to successfully detect whether the string is the committed one or not. When Alice and Bob are computationally unbounded, i.e., under the information-theoretic setting, it is well known that even a single bit commitment is impossible when the channel available to Alice and Bob is noiseless. Noisy channels, however, offer the potential of non-zero commitment rate, and thus, are a valuable resource. We study information-theoretically secure commitment over noisy discrete memoryless channels (DMCs). The largest commitment throughput over noisy channels is called the commitment capacity or simply capacity. In this work, we completely characterize via a single-letter expression, the commitment capacity of DMCs under general cost constraints; this generalizes the previously known result in the absence of such cost constraints. We show that cost constrained commitment capacity of any given DMC can significantly differ from its unconstrained value. We also present a dual capacity characterization in terms of output distributions. Interestingly, we show that every input distribution achieving the capacity results in the same output distribution; the latter is the unique optimizer of our dual capacity expression.