Singapore, Singapore

Kailash Sivanesan


Average Co-Inventor Count = 4.3

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2021-2023

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4 patents (USPTO):Explore Patents

Title: Innovations of Kailash Sivanesan

Introduction

Kailash Sivanesan is a prominent inventor based in Singapore, known for his contributions to secure multiparty computation and blockchain technology. With a total of four patents to his name, he has made significant strides in enhancing data integrity and job performance in computational systems.

Latest Patents

Kailash's latest patents include "Systems and methods for dynamic job performance in secure multiparty computation." This invention outlines a method for processing jobs on data inputs by splitting them into partial data inputs and generating multiple jobs for efficient processing. It also includes a mechanism to adjust worker processes based on various parameters such as computation requirements and network bandwidth. Another notable patent is "Systems and methods for preserving data integrity when integrating secure multiparty computation and blockchain technology." This patent describes a method for splitting data into secret shares and assigning them to compute nodes within a blockchain network, ensuring data integrity through a message authentication code.

Career Highlights

Kailash Sivanesan is currently employed at Acronis International GmbH, where he continues to innovate in the field of secure computing. His work focuses on developing systems that enhance the security and efficiency of data processing.

Collaborations

Kailash has collaborated with notable colleagues such as Mark A Will and Sanjeev Solanki, contributing to a dynamic work environment that fosters innovation and creativity.

Conclusion

Kailash Sivanesan's work exemplifies the intersection of technology and security, making significant contributions to the fields of secure multiparty computation and blockchain. His innovative patents reflect a commitment to advancing data integrity and computational efficiency.

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