In the face of AI-powered surveillance, we need decentralized confidential computing

In the face of AI-powered surveillance, we need decentralized confidential computing

When Oracle AI CTO Larry Ellison introduced his idea of a global network using AI for surveillance to encourage citizens to stay on their “best behavior,” critics quickly drew parallels to George Orwell’s 1984. Many described Ellison’s vision as dystopian, highlighting the risks of mass surveillance on privacy and the psychological pressure it places on individuals. This surveillance could deter people from expressing dissent or engaging in protests.

However, the alarming fact is that AI-driven mass surveillance is already in use. For instance, during the recent Summer Olympics in Paris, the French government hired four tech firms—Videtics, Orange Business, ChapsVision, and Wintics—to deploy AI-powered video analytics. These systems monitored public behavior and alerted security authorities.

The Rise of AI-Powered Surveillance: Legislation and Deployment

Legislation passed in 2023 allowed the use of advanced AI software to analyze public data. As the first European Union nation to legalize AI-powered surveillance, France exemplifies the growing trend of video analytics.

Surveillance isn’t a new concept. The UK began installing CCTV in cities in the 1960s. By 2022, 78 out of 179 OECD countries had adopted AI-based public facial recognition systems. As AI technology advances, the demand for large-scale data analysis will continue to rise.

Governments have often partnered with private companies to enhance mass surveillance systems. The Paris Olympics provided these tech firms an opportunity to refine their AI models by accessing information about individuals’ locations and behaviors, impacting millions attending the event.

Balancing Privacy and Public Safety in AI Surveillance

Privacy advocates argue that video surveillance restricts people from living freely and without fear. Policymakers, on the other hand, claim these measures are necessary for public safety. They believe surveillance can also hold authorities accountable by requiring police to wear body cameras.

The core issue revolves around whether tech companies should have access to public data and how much sensitive information they can securely store and share among different parties. This challenge of data storage and transfer is one of the most significant issues facing our generation.

Decentralized Confidential Computing: A Solution to Data Privacy

Decentralized Confidential Computing (DeCC) presents a solution to ongoing concerns about AI and data privacy. Many AI models, like Apple Intelligence, rely on Trusted Execution Environments (TEEs). These systems have single points of failure and require third-party trust. DeCC aims to eliminate these vulnerabilities by establishing a decentralized, trustless system for data analysis and processing.

Moreover, DeCC enables data analysis without decrypting sensitive information. For example, a video analytics tool built on a DeCC network could alert authorities of security threats without exposing individuals’ sensitive data.

Innovations in Decentralized Confidential Computing

Various decentralized computing techniques are being explored, including Zero-Knowledge Proofs (ZKPs), Fully Homomorphic Encryption (FHE), and Multi-Party Computation (MPC). These methods aim to verify essential information without revealing sensitive details.

Among these, MPC stands out by allowing the creation of Multi-Party Execution Environments (MXE). These virtual, encrypted execution containers can perform computations in a fully confidential manner.

In practice, this technology allows facial recognition to be conducted while keeping data hidden from the parties processing it. The analytics derived from this data can then be securely shared with relevant security authorities. This approach introduces transparency and accountability into surveillance while protecting confidential information.

Looking Forward: The Future of Data Privacy

Though decentralized confidential computing is still developing, it highlights the risks of relying on trusted systems. It offers a promising alternative for data encryption. As machine learning continues to integrate into sectors like city planning, medicine, and entertainment, protecting user data is crucial.

For these applications, training models rely heavily on user information. DeCC will play a fundamental role in safeguarding privacy. To avoid a dystopian future, decentralizing AI will be key to ensuring data protection and privacy in the age of surveillance.

The post In the face of AI-powered surveillance, we need decentralized confidential computing appeared first on CryptoSlate.

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