News

Acompany Co., Ltd. (Headquarters: Nagoya, Aichi, Japan; CEO: Ryosuke Takahashi; hereinafter "Acompany") will participate as a Gold Sponsor of the Confidential Computing Summit 2026, taking place June 23–24, 2026 in San Francisco, USA, where it will deliver two sessions.

The Confidential Computing Summit is the leading global forum dedicated to Confidential Computing and Confidential AI, co-hosted by The Linux Foundation and OPAQUE. Now in its fourth year, the 2026 edition will be held at the historic San Francisco Mint, bringing together 400+ enterprise decision-makers, technologists, and policymakers from around the world.

Official site
 https://events.linuxfoundation.org/confidential-computing-summit/

At the summit, Acompany will present in a sponsor session and an accepted breakout session, sharing the latest trends in Sovereign AI and Confidential Computing in Japan, as well as a large-scale Data Clean Room case study with KDDI.

Sessions

Session 1 (Sponsor Session)

Title
Confidential Computing for Sovereign AI: The Emerging Opportunity in Japan

Speakers
Takeharu Kondo (Co-Founder / CRDO), Shuzo Ueki (CFO)

Summary
As generative AI and AI agents rapidly evolve, interest in Sovereign AI is growing worldwide. In Japan, demand is increasing for trusted AI infrastructure across sectors such as defense, healthcare, manufacturing, telecommunications, and critical infrastructure. This session introduces the latest trends in Sovereign AI and Confidential Computing (CC) in Japan.

Japan is advancing Confidential Computing enabled data centers, secure data collaboration platforms, and privacy-preserving AI environments. Expectations are also growing for CC adoption in government-led projects and critical infrastructure sectors.

In this session, we discuss the opportunities surrounding Sovereign AI in Japan for AI model providers, cloud providers, GPU vendors, and data center operators. Through real-world examples, we explore how CC is becoming a core technology for building Trusted AI Infrastructure in the AI agent era.

Session 2 (Breakout Session)

Title
A Large-Scale Data Clean Room Case Study in Japan: Confidential Computing and Privacy Regulations

Speakers
Takao Takenouchi, Ph.D. (VP of Public Affairs), Takeharu Kondo (Co-Founder / CRDO)

Summary
AI model advancement demands cross-enterprise data collaboration, but strict privacy regulations create barriers. This session explores a commercialized Data Clean Room in Japan by Acompany and KDDI, a Fortune Global 500 telecom company.

We will share the architecture enabling secure data matching and privacy-preserving AI development. We detail how this satisfies the strict third-party data transfer restrictions under Japan's Act on the Protection of Personal Information (APPI). By keeping the calculation process protected, enterprises can jointly analyze sensitive large-scale datasets—including personal and location data—without exposing raw information to partners.

Furthermore, we explore the relationship between policy discussions and CC in Japan. With CC recognized as an essential data security technology in public and private sectors, we discuss the potential for market expansion. We provide insights into how bridging governance and technology creates a scalable confidential AI infrastructure. Targeting architects and business leaders, this session offers best practices for navigating complex privacy and security rules globally.

Event Overview

• Event: Confidential Computing Summit 2026

• Dates: June 23–24, 2026

• Venue: San Francisco Mint (San Francisco, USA)

• Hosts: The Linux Foundation / OPAQUE (co-hosts)

• Official site: https://events.linuxfoundation.org/confidential-computing-summit

About Acompany

• Company: Acompany Co., Ltd.

• CEO: Ryosuke Takahashi

• Headquarters: Nittochi Nagoya Bldg. 7F, 2-1-1 Sakae, Naka-ku, Nagoya, Aichi, Japan

• Founded: June 2018

• URL: https://en.acompany.tech/

• Business: Products and technologies related to secure computation (privacy-enhancing computation), and consulting services for the secure use of confidential data

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