"Advances in Computer and Communication" Article Recommendation | How Privacy-Enhancing Technologies are Reshaping the Future of AI Risk Control? A Silent Revolution Concerning Data Security and Efficiency

"When an AI risk control system can predict your every move, what privacy do you have left?" "In this era of data-driven decision-making, are we trading personal privacy for so-called security?" These questions are not only about technological innovation but also determine the baseline of human dignity in the digital age.

In the recent paper "Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems" published in Advances in Computer and Communicationby Mingjie Chen from Carnegie Mellon University, we are shown how to achieve the perfect balance between security and privacy through cutting-edge technologies.

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Privacy-Enhancing Technologies: The "Safety Valve" for AI Risk Control

Traditional AI risk control systems are a double-edged sword; while they enhance detection efficiency, they also bring significant privacy leakage risks. The massive amounts of user data accumulated by enterprises become a "gold mine" in the eyes of hackers. The emergence of Privacy-Enhancing Technologies (PETs) acts like a sturdy "safety valve" installed on this vulnerable system. Through innovative technologies such as federated learning, homomorphic encryption, and differential privacy, the system can complete model training without the data leaving the local environment, achieving the revolutionary breakthrough of "data being usable but invisible."

Data Breach Crisis: The Defining Moment for PETs

In 2023, global data breach incidents caused losses as high as $445 million, and the widespread deployment of AI risk control systems poses unprecedented challenges to privacy protection. Mingjie Chen's research indicates that risk control systems employing differential privacy can reduce the risk of privacy leakage to 1/10th of traditional methods while maintaining a model accuracy rate of no less than 95%. This is not only a technological breakthrough but also a complete disruption of the traditional perception of the "zero-sum game between privacy and privacy" security."

Implementation Challenges: The Hard Journey from Theory to Practice

Although PETs show great potential, their industrial application still faces three major challenges: the balance between computational efficiency and accuracy, technical barriers to cross-system compatibility, and the game between compliance costs and benefits. As research shows, even the most advanced fully homomorphic encryption technology currently increases computational latency by 3-5 times, which requires the dual drive of algorithm optimization and hardware innovation.

Future Outlook: The Privacy-First AI Paradigm

Privacy-enhancing technologies are catalyzing a paradigm shift in AI risk control. They can not only provide the financial industry with GDPR-compliant intelligent anti-fraud solutions but also build secure bridges for medical data sharing, and are likely to become a cornerstone technology for digital identity protection in the metaverse era. This "privacy by design" philosophy will redefine the trust relationship between humans and machines.

"The best technology does not replace humans but allows humans to benefit from technology more safely." As AI sweeps across all industries today, privacy-enhancing technologies are like a beacon in the dark, guiding us towards an intelligent yet trustworthy digital future. Only when we can protect the most precious right to privacy while enjoying the conveniences of AI can technology truly fulfill its purpose of empowering humanity.

In this revolution concerning the digital existence of every individual, are we ready to embrace a new era where we can "have our cake and eat it too"?

The study was published in Advances in Computer and Communication

https://www.hillpublisher.com/ArticleDetails/5435

How to cite this paper

Mingjie Chen. (2025) Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems. Advances in Computer and Communication, 6(4), 173-177.

DOI: http://dx.doi.org/10.26855/acc.2025.10.003