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Title |
Analysis of Two Public-Key Encryptions based on Lattice Problems |
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Author |
Jinsu Kim |
| Citation |
Vol. 19 No. 11 pp. 126-131 |
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Abstract |
A study on lightweight public key cryptography is one of the important task for cloud computing and IoT environment. Recently, with the advance in quantum computing, many people in various fields are preparing their information systems to be secure for quantum attacks. In this background, we propose and analyze two public-key encryption schemes based on AGCD and LWE in order to see their possibility as lightweight PKE. We apply existing attacks of AGCD and LWE more precisely and concretely. Finally, we show how to choose parameters of the schemes under the consideration of their decryption correctness and security against the proposed attacks. We expect this work would be a post stone for designing lightweight public-key cryptosystem resistant in quantum world. |
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Keywords |
Public-key Encryption, LWE, AGCD, lightweight |
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Title |
Path Planning for Autonomous Mobile Robots |
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Author |
Khalid Bashir, Sohail Abbasi, Waqas Nawaz Khokhar |
| Citation |
Vol. 19 No. 11 pp. 132-138 |
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Abstract |
Building autonomous and intelligent robots has been an elusive dream for researchers for some time. Simultaneous Localization and Mapping (SLAM) systems have contributed towards achieving this goal by making robots better in navigating through complex environments. Until now it has only been possible to train and teach robots to move around in particular environments using a certain set of rules and heuristics. With the sudden surge in interest in AI and Machine Learning, a lot of effort has been put in into making robots intelligent and for them to automatically learn their paths in unknown environments (also referred to as Path Planning). This however has been met with mixed results as either the solution proposed is not too practical (e.g. requires too much training) or has limited success (e.g. works in specific environments). In this research, we develop a novel autonomous path planning framework using Deep Learning which can learn to navigate in unknown environments. The system has been tested on state-of-the-art Active Vision Dataset with promising results. |
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Keywords |
Autonomous Robots, Robot Navigation, Path Planning, Simultaneous Localization and Mapping, Convolutional Neural Networks. |
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