Delivered on October 20, 2025
Location Privacy and AI by Privacy Tech @ Kinki University Seminar
Speaker:
Privacy Tech, Inc.
CEO Daisuke Yamashita
Affiliation and position information is as of the time of distribution
Location Information Business Seminar held at Kinki University on July 14, 2025
This seminar discussed the business possibilities of utilizing location information data.
We will introduce the content of each seminar in several installments.
Whether it is personal information or not (personal information), location data is highly confidential data, and consideration of privacy protection is essential. When handling this data in business, utilizing AI makes it possible to explain the "risks and countermeasures" of that business. In this presentation, we will hear proposals on how to overcome the barriers between utilizing AI and business development.
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"Defense" and "Offense" are Essential for Data Utilization: Avoiding Backlash and Improving Productivity in the AI Era
Daisuke Yamashita of Privacy Tech Inc. (former LBMA Japan executive) spoke at CEATEC 2025 about the most important themes in utilizing location data: "risk management (defense)" and "AI-driven productivity improvement (offense)."
The Risks of Using Location Data: Legal Compliance Is Not Enough
By its very nature, location data is highly risky, raising concerns about private tracking and linking. Even when companies comply with laws and guidelines, "backlash" cases continue to occur, creating a vicious cycle of service outages and stagnation of innovation across the industry.
Yamashita points out that the fundamental cause of the increased risk of backlash is that traditional risk assessments are biased toward compliance with laws and regulations (jurisprudence) and lack consideration for "user sentiment." While complying with laws and regulations is a minimum requirement, it is essential to incorporate an ethical perspective on how data utilization is perceived by the public, in addition to considerations of personal information and privacy tech.
Hard to Project Promotion: How to Overcome the Internal "Explanation Barrier"
When a data utilization project is approved internally, an "explanation barrier" often arises between the field and legal and management teams. In particular, projects often stall due to a lack of clear answers to the question, "Is there really a risk of a backlash?"
To overcome this barrier and smoothly promote data utilization, multifaceted accountability that covers the following elements is required.
- Visualizing data flow: Clearly show the flow of data.
- Confirming legal compliance: Demonstrate that there are no legal risks.
- Considering user sentiment: Explain that the design is sensitive to people's feelings.
- Balancing risk and reward: Clearly weigh the business benefits against potential risks.
This demonstrates to legal and management teams that the project is being promoted while keeping risks under control, enabling company-wide buy-in.
Japan's Growth Strategy and "Mechanism Building" in the AI Era
Yamashita positioned generative AI (GPT) as a "general-purpose technology" comparable to the emergence of electricity and automobiles. AI offers the opportunity to fundamentally transform not only specific industries but all industrial structures, dramatically improving Japan's sluggish labor productivity.
However, simply introducing AI will not increase productivity. Building a "mechanism" that converts AI-generated work hours into highly productive, new value creation is the key to regaining international competitiveness.
LBMA Japan is taking on this very "mechanism building" role, and rather than waiting for regulations, has developed a system in which the industry itself formulates and adheres to guidelines. Yamashita's company has applied this know-how to develop an "AI agent" that assesses and audits the risks of data utilization and AI implementation, streamlining complex business processes and strongly supporting the social implementation of AI by Japanese companies.
Summary
In today's world where data utilization is unavoidable, the key to success lies not only in compliance with laws and regulations but also in thorough risk management that takes user sentiment into consideration. The formulation of autonomous industry rules, as promoted by LBMA Japan, and the efficient use of AI in governance are essential strategies for companies to avoid the risk of being criticized and to achieve both improved productivity and innovation in the age of AI.
Going forward, companies will be required to think outside the box and "create systems" for themselves.