[ad_1] Introduction: Secure enclaves for trusted execution have gained significant attention in recent years due to the growing concerns about the security and privacy of sensitive data in cloud computing environments. Secure enclaves, also known…
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[ad_1] Introduction In recent times, with the rapid advancements in technology and the widespread use of cloud computing, the need for secure and confidential data processing has become more important than ever. Encrypted computation is…
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[ad_1] Introduction Secure aggregation for distributed learning is a critical component in the field of machine learning and data privacy. With the increasing amount of data being collected and processed in various applications, the need…
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[ad_1] Introduction In today’s digital age, the collection, analysis, and storage of personal data have become ubiquitous in various applications such as healthcare, finance, and social media. With the increasing concerns about privacy and data…
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[ad_1] Introduction The increasing demand for data privacy and security in collaborative learning environments has led to the development of secure multi-party computation (MPC) techniques. These techniques allow multiple parties to jointly compute a function…
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[ad_1] Introduction Privacy-preserving machine learning for secure computation is a rapidly growing field in computer science and data privacy. With the increasing amount of sensitive data being collected and analyzed, ensuring the privacy and security…
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[ad_1] Introduction Non-convex optimization is a challenging field in mathematics and computer science that deals with finding the optimal solutions for problems that do not have convex objective functions. Local minima are a common issue…
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[ad_1] Introduction Convex optimization is a powerful mathematical tool that has been widely used in various fields such as machine learning, signal processing, control systems, and operations research. It involves the optimization of convex objective…
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[ad_1] Introduction: In recent years, there has been a growing interest in submodular optimization for diversity and coverage in various fields such as machine learning, data mining, and artificial intelligence. Submodular functions have the property…
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[ad_1] Introduction Dictionary learning is a powerful technique used in machine learning and signal processing for basis discovery. It involves the process of learning a dictionary that can effectively represent a set of data samples…
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