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Technology
AI Ecosystem AI Models AI technology AI Tool Computing Data Privacy Data Sets DeFi privacy protection secure computing Technology

The Role of Confidential Computing in AI and Web3

 

 
The rise of artificial intelligence (AI) has amplified the demand for privacy-focused computing technologies, ushering in a transformative era for confidential computing. At the forefront of this movement is the integration of these technologies within the AI and Web3 ecosystems, where maintaining privacy while enabling innovation has become a pressing challenge. A major event in this sphere, the DeCC x Shielding Summit in Bangkok, brought together more than 60 experts to discuss the future of confidential computing.

Pioneering Confidential Computing in Web3

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.

Innovations in Privacy and Scalability

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.

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Cyber Security Data protection defense against cyber attacks Digital Security Information Security internet Security Online Privacy Online Safety secure computing threat prevention

Understanding Cold Boot Attacks: Is Defense Possible?

 

Cold boot attacks represent a sophisticated form of cyber threat that specifically targets a computer's Random Access Memory (RAM), presenting a substantial risk to information security. It is imperative to comprehend the mechanics of cold boot attacks and the potential hazards they pose to take necessary precautions. However, if you become a target, mitigating the attack proves extremely challenging due to the requisite physical access to the computer.

Cold boot attacks, although less common, emerge as a potent cyber threat, particularly in their focus on a computer's RAM—a departure from the typical software-centric targets. These attacks have a physical dimension, with the primary objective being to induce a computer shutdown or reset, enabling the attacker to subsequently access the RAM.

When a computer is shut down, one anticipates that the data in RAM, including sensitive information like passwords and encryption keys, vanishes. However, the process is not instantaneous, allowing for the potential retrieval of data remaining in RAM, albeit for a brief period. A critical element of cold boot attacks is the necessity for physical access to the targeted device, elevating the risk in environments where attackers can physically approach machines, such as office spaces. Typically, attackers execute this attack using a specialized bootable USB designed to duplicate the RAM contents, enabling the device to reboot according to the attacker's intentions.

Despite the ominous nature of cold boot attacks, their execution requires a significant investment of skills and time, making it unlikely for the average person to encounter one. Nevertheless, safeguarding your computer from both cyber and physical threats remains a prudent practice.

The essence of a cold boot attack lies in exploiting a unique feature of RAM—the persistence of data even after the computer is powered off. Understanding this attack involves recognizing what happens to the data in RAM during a computer shutdown. The attacker gains physical access to the computer and utilizes a specialized USB to force a shutdown or restart. This USB facilitates the booting or dumping of RAM data for analysis and data extraction. Additionally, malware can be employed to transfer RAM contents to an external device.

The data collected in cold boot attacks encompasses a spectrum from personal information to encryption keys. Speed is paramount in this process, as prolonged power loss to RAM results in data corruption. These attacks pose a significant threat due to their ability to bypass conventional security software, rendering antivirus programs and encryption tools ineffective against them.

To counter cold boot attacks, a combination of physical and software strategies is necessary. Securing the physical space of the computer, employing encryption, and configuring BIOS or UEFI settings to prevent external device booting are recommended. Addressing data remanence is crucial, and techniques like memory scrubbing can be employed to clear RAM of sensitive data after shutdown or reset.

In conclusion, robust defenses against cold boot attacks involve a multi-faceted approach, including strong encryption, physical security measures, and regular updates. Understanding the intricacies of RAM and its data persistence underscores the need for dynamic and proactive cybersecurity measures. Adapting to evolving cyber threats and strengthening defenses is essential in building a resilient digital space that protects against not only cold boot attacks but a range of cyber threats.
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