Kansas City, MO, United States of America

Stephen Scott Penrod


Average Co-Inventor Count = 8.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Innovations of Stephen Scott Penrod

Introduction

Stephen Scott Penrod is an accomplished inventor based in Kansas City, MO (US). He has made significant contributions to the field of secure multi-party computations, particularly in the context of neural networks. His innovative work has led to the development of a unique patent that addresses critical challenges in data privacy and security.

Latest Patents

Penrod holds a patent for "Systems and methods for providing a multi-party computation system for neural networks." This patent describes a system and method that enables secure multi-party computations. The system performs operations such as establishing an API for coordinating joint operations between two access points. These operations are essential for performing a secure prediction task, allowing both parties to compute private data without accessing each other's information. The patent also includes features for managing authentication and authorization of communications, ensuring a secure environment for data processing.

Career Highlights

Stephen Scott Penrod is currently employed at Tripleblind, Inc., where he continues to innovate in the field of secure computations. His work is pivotal in advancing technologies that prioritize data privacy and security. With a focus on neural networks, Penrod's contributions are shaping the future of secure data processing.

Collaborations

Some of Penrod's notable coworkers include Babak Poorebrahim Gilkalaye and David Norman Wagner. Their collaborative efforts contribute to the innovative environment at Tripleblind, Inc., fostering advancements in secure computation technologies.

Conclusion

Stephen Scott Penrod's work exemplifies the importance of innovation in secure data processing. His patent and contributions to the field highlight the ongoing need for advancements in privacy and security within computational systems.

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