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Mahimna Kelkar

As seen in: arXiv, CoinDesk
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Articles

Lanturn: Measuring Economic Security of Smart Contracts Through Adaptive Learning

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By Kushal Babel, Yan Ji, Mahimna Kelkar, Mojan Javaheripi
| eprint.iacr.org
Paper 2023/1338 , Cornell Tech, IC3 , University of California San Diego , Cornell Tech, IC3 , Cornell Tech, IC3 , University of California San Diego , Cornell Tech, IC3 We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts.
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One-Message Secure Reductions: On the Cost of Converting Correlations

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By Yuval Ishai, Mahimna Kelkar, Varun Narayanan, Liav Zafar
| eprint.iacr.org
Paper 2023/1225 , Technion – Israel Institute of Technology , Cornell University , University of California, Los Angeles , Technion – Israel Institute of Technology Correlated secret randomness is a useful resource for secure computation protocols, often enabling dramatic speedups compared to protocols in the plain model. This has motivated a line of work on identifying and securely generating useful correlations.
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Buying Time: Latency Racing vs. Bidding in Transaction Ordering

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By Jan Christoph, Mahimna Kelkar, Jan Schlegel, Edward Felten
| arXiv
arXiv:2306.02179 (cs) [Submitted on 3 Jun 2023 (v1), last revised 16 Aug 2023 (this version, v2)] Download PDF Subjects: Computer Science and Game Theory (cs.GT); Cryptography and Security (cs.CR); Theoretical Economics (econ.TH) Cite as: arXiv:2306.02179 [cs.GT] (or arXiv:2306.02179v2 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2306.02179 Submission history From: Mahimna Kelkar [ view email] [v1] Sat, 3 Jun 2023 19:20:39 UTC (322 KB) [v2] Wed, 16 Aug 2023 14:33:49 UTC (49 KB)...
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