Lisa Loud, Executive Director of the Secret Network Foundation, emphasized in her keynote that Secret Network has been pioneering confidential computing in Web3 since its launch in 2020. According to Loud, the focus now is to mainstream this technology alongside blockchain and decentralized AI, addressing concerns with centralized AI systems and ensuring data privacy.
Yannik Schrade, CEO of Arcium, highlighted the growing necessity for decentralized confidential computing, calling it the “missing link” for distributed systems. He stressed that as AI models play an increasingly central role in decision-making, conducting computations in encrypted environments is no longer optional but essential.
Schrade also noted the potential of confidential computing in improving applications like decentralized finance (DeFi) by integrating robust privacy measures while maintaining accessibility for end users. However, achieving a balance between privacy and scalability remains a significant hurdle. Schrade pointed out that privacy safeguards often compromise user experience, which can hinder broader adoption. He emphasized that for confidential computing to succeed, it must be seamlessly integrated so users remain unaware they are engaging with such technologies.
Shahaf Bar-Geffen, CEO of COTI, underscored the role of federated learning in training AI models on decentralized datasets without exposing raw data. This approach is particularly valuable in sensitive sectors like healthcare and finance, where confidentiality and compliance are critical.
Henry de Valence, founder of Penumbra Labs, discussed the importance of aligning cryptographic systems with user expectations. Drawing parallels with secure messaging apps like Signal, he emphasized that cryptography should function invisibly, enabling users to interact with systems without technical expertise. De Valence stressed that privacy-first infrastructure is vital as AI’s capabilities to analyze and exploit data grow more advanced.
Other leaders in the field, such as Martin Leclerc of iEXEC, highlighted the complexity of achieving privacy, usability, and regulatory compliance. Innovative approaches like zero-knowledge proof technology, as demonstrated by Lasha Antadze of Rarimo, offer promising solutions. Antadze explained how this technology enables users to prove eligibility for actions like voting or purchasing age-restricted goods without exposing personal data, making blockchain interactions more accessible.
Dominik Schmidt, co-founder of Polygon Miden, reflected on lessons from legacy systems like Ethereum to address challenges in privacy and scalability. By leveraging zero-knowledge proofs and collaborating with decentralized storage providers, his team aims to enhance both developer and user experiences.
As confidential computing evolves, it is clear that privacy and usability must go hand in hand to address the needs of an increasingly data-driven world. Through innovation and collaboration, these technologies are set to redefine how privacy is maintained in AI and Web3 applications.
Automatic Content Recognition (ACR) is the invisible eye that tracks everything you watch on your smart TV. Whether it’s a gripping drama, a cooking show, or a late-night talk show, your TV is quietly analyzing it all. ACR identifies content from over-the-air broadcasts, streaming services, DVDs, Blu-ray discs, and internet sources. It’s like having a digital detective in your living room, noting every scene change and commercial break.
Ever notice how ads seem eerily relevant to your interests? That’s because of Advertisement Identification (AdID). When you watch a TV commercial, it’s not just about the product being sold; it’s about the unique code embedded within it. AdID deciphers these codes, linking them to your viewing history. Suddenly, those shoe ads after binge-watching a fashion series make sense—they’re tailored to you.
Manufacturers and tech companies profit from your data. They analyze your habits, preferences, and even your emotional reactions to specific scenes. This information fuels targeted advertising, which generates revenue. While it’s not inherently evil, the lack of transparency can leave you feeling like a pawn in a digital chess game.
Turn Off ACR: Visit your TV settings and disable ACR. By doing so, you prevent your TV from constantly analyzing what’s on your screen. Remember, convenience comes at a cost—weigh the benefits against your privacy.
AdID Management: Reset your AdID periodically. This wipes out ad-related data and restricts targeted ad tracking. Dig into your TV’s settings to find this option.
Voice Control vs. Privacy: Voice control is handy, but it also means your TV is always listening. If privacy matters more, disable voice services like Amazon Alexa, Google Assistant, or Apple Siri. Sacrifice voice commands for peace of mind.
Different smart TV brands have varying privacy settings. Here’s a quick guide:
Amazon Fire TV: Navigate to Settings > Preferences > Privacy Settings. Disable “Interest-based Ads” and “Data Monitoring.”
Google TV: Head to Settings > Device Preferences > Reset Ad ID. Also, explore the “Privacy” section for additional controls.
Roku: Visit Settings > Privacy > Advertising. Opt out of personalized ads and reset your Ad ID.
LG, Samsung, Sony, and Vizio: These brands offer similar options. Look for settings related to ACR, AdID, and voice control.
Your smart TV isn’t just a screen; it’s a gateway to your personal data. Be informed, take control, and strike a balance. Enjoy your favorite shows, but remember that every episode you watch leaves a digital footprint. Protect your privacy—it’s the best show you’ll ever stream.