Abstract: In this paper, we present communication complexity results of several chosen-ciphertext attack (CCA2) secure public key cryptosystems that are resilient to certain classes of secret key leakage attacks. For this purpose, we propose a communication complexity measure for public key cryptosystems that equals the total amount of bits involving the receiver’s public key and a ciphertext transmission by the sender.

Abstract: Lattice-based cryptography is one of the leading candidates for post-quantum cryptography and it is primarily based on the hardness of the Learning with Errors (LWE) problem. However, to ensure that the underlying problem instance used in the cryptographic instance is hard, it is important that the LWE parameters are set properly. These parameters are set based on attempts to solve the LWE. These attempts are mainly the primal attack and dual attack. In primal attack, we solve LWE by solving the Bounded Distance Decoding (BDD) problem. On the other hand, dual attack solves LWE by solving the Shortest Integer Solution (SIS) problem.

Abstract: In this talk, I will discuss applications of cryptography in rational file sharing protocols.

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Abstract: For this talk, we look into solving the satisfiability problem using string Rewriting P system with communication by request.

Abstract: The field of Quantum Computing has seen a rise in both theoretical research and engineering development in the past decade. Several technological companies, including IBM and Google, and educational institutions are currently constructing quantum computers. To augment these development, there is academic interest in developing systems to automate the design of quantum circuits and to simulate quantum circuits on conventional (i.e. non-quantum computers) hardware. Quantum algorithms exploit massive parallelism to solve problems faster than their best known classical counterparts. In order to keep up with quantum parallelism, previous research on quantum circuit simulation on conventional hardware employed data structures to compress exponentially growing matrices, such as expanded binary decision diagrams [Zulehner, Wille, et al., 2020][3]. Matrices are usually compressed into decision-diagrams by extracting redundancies in the structure and content of matrices. This produces hardware-dependent inaccuracies and missed redundancies since conventional computers might approximate the same irrational-complex number as different finite floating numbers in different steps of the computation. An iteration of quantum decision-diagrams shows that it is possible to achieve optimal matrix compression without loss of information [Niemman et al., 2020][2]. Almost parallel to progress in the development of quantum computers is the progress in the development of highly capable consumer-grade GPUs and general purpose computation on GPUs [Kirk and Hwu, 2012][1].Previous simulators, accessible in IBM’s qiskit and Google’s cirq, rely on HPC’s with multiple GPUs. By exploiting previous research on lossless compression(and by designing new compression techniques+identifying more opportunities for parallelism) and increasingly more capable consumergrade GPUs, it is perhaps now possible to build a practical and compact quantum circuit simulator that researchers can run locally on their personal computers. (also possible to add this simulator to the open-source qiskit SDK)

Abstract: The goal of indoor localization is to estimate the location of a person or object inside a building, where the applicability of the GPS signal is compromised. One popular approach for indoor localization is Wi-Fi Fingerprinting, due to the pervasiveness of Wi-Fi technology. Fingerprint-based localization captures the relation between the Received Signal Strength indicator received from different Wireless Access Points (APs) and user location by constructing a database of RSS fingerprints of the APs at different discrete locations during the offline or calibration phase, then comparing the RSS indicator received from APs to the fingerprint during the online phase. The fingerprint closest to the received signal is the estimated location. The approach is envisioned to benefit from utilizing quantum fingerprinting, in which the minimum amount of communication could be exponentially smaller than the classical fingerprinting. In this talk, we discuss the applicability of quantum algorithms for indoor localization