In this enlightening panel, we explore the technical innovations that create trusted execution environments and foster data partnerships across functions, departments, and organizations, fostering a trusted and resilient data economy. Key highlights include:
Collaboration Between PETs and Confidential Computing:
Understand the need for synergy between these technologies to cater to enterprise-level adoption. Explore how PETs and confidential computing enhance data integrity and security.
Unlocking Sensitive Data Sets:
Whether dealing with personal identifiable information, health records, or financial reports, data integrity is invaluable. Discover a privacy toolkit that securely unlocks these data sets, empowering organizations to navigate privacy complexities.
Enhancing Trust and Resilience in Data Economy:
Embrace these innovations to create a stronger, transparent data ecosystem that thrives on trust and resilience.
Attendees will leave equipped with the understanding of how PETs and confidential computing go hand-in-hand and how these technologies increase data utility and data integrity, ultimately contributing to a stronger, more transparent data ecosystem that thrives on trust and resilience.
Jay Prakash
Silence Laboratories
Website: https://www.silencelaboratories.com/
Silence Laboratories, a privacy technology and infrastructure company based in Singapore, provides algorithms and product suites using distributed computation and authorization technologies, particularly multi-party computation (MPC).
Silence Laboratories (SL) boasts one of the fastest threshold signatures and authorization libraries in production, safeguarding digital assets worth billions. SL’s libraries and infrastructure enable enterprises to collaborate on data without transferring it to a trusted party, thereby opening avenues for monetization and the development of innovative products. With partners across various sectors including finance, digital assets, and data industries, SL is creating a data-sharing and computation ecosystem where consent and privacy-preserving analysis are mathematically coupled, ensuring compliance and security. SL is a preferred choice by enterprises owing to its developer-friendly libraries and application-agnostic privacy-preserving collaboration tools.
