How FHE and Confidential Computing Enables Scalable, Confidential AI in Beldex

5 min readOct 16, 2024

Scalable, Confidential AI in the Beldex Ecosystem

Fully Homomorphic Encryption (FHE) is transforming the landscape of confidential AI by enabling scalable, confidential computations. FHE allows data to remain encrypted throughout processing, ensuring that sensitive information is never exposed, even during complex AI operations. This technology not only safeguards user confidentiality but also supports the development of robust, decentralized AI solutions that can operate seamlessly on encrypted data. By integrating FHE, Beldex is pushing the boundaries of confidentiality and scalability, paving the way for innovative, secure AI applications across its ecosystem.

Understanding Fully Homomorphic Encryption (FHE)

Fully Homomorphic Encryption (FHE) revolutionises data security. Imagine using complex computations and analytics to maximise your data’s potential while keeping it secured and safe, even in untrustworthy environments.

Companies today have data dispersed across hybrid multi-cloud infrastructures, posing security and confidentiality threats. Traditional encryption requires data decryption for computers and operations, placing sensitive data at risk. Cloud computing and third-party cooperation previously had this drawback.

FHE disrupts this paradigm by encrypting data throughout, resulting in true zero trust. Zero trust in this context means that no trust is required to ensure that the system or application works as it is intended to. This innovative approach secures data transit and processing on untrustworthy sites without compromise. Even if the data were to be intercepted during transit or processing, a malicious individual or program would not be able to decipher it. This means that computational entities can do high-value data analytics and data processing without disclosing sensitive information.

Fully homomorphic encryption revolutionises data security. It changes data management, enabling zero trust in a future when data confidentiality and security are extremely crucial.

Confidential AI: A Game-Changer for BChat

BChat, like many online platforms, faces the constant challenge of maintaining a safe and positive environment for its users. This is a challenge as BChat is a decentralized application governed only by the decentralized Beldex network. This is where confidential AI steps in as a game-changer for content moderation. Here’s how:

1. Enhanced Accuracy and Efficiency:

Automated Content Screening: AI algorithms can analyze massive volumes of text and images in real-time, flagging potentially harmful content such as hate speech, spam, and explicit material. This significantly reduces the workload on human moderators and allows for quicker action. Especially, in a decentralized self-governing application such as BChat where the prospect of employing human moderators is non-existent, AI models that take up this work can help such applications to comply with regulations.

Contextual Understanding: Advanced AI models can understand the nuances of different languages, including slang, sarcasm, and intent, to better assess the true nature of a message. This helps to avoid false positives and ensures that legitimate expression is not stifled.

2. Proactive Prevention:

Pattern Recognition: Much like human moderators, AI can identify patterns and trends in user behaviour and content, allowing it to predict and prevent potential issues before they escalate. This could involve identifying users who are likely to engage in harmful behaviour or detecting emerging forms of hate speech, unauthorized (with respect to regulations of a specific region or country) or illicit activities.

Personalized Interventions: AI can tailor moderation responses to individual users and situations. For example, a first-time offender might receive a warning, while a repeat offender might face stricter consequences.

3. Protecting User Confidentiality:

On-Device Processing: Confidential AI allows for content moderation to be performed directly on the user’s device, minimizing the need to transmit sensitive data to the cloud. This enhances confidentiality and reduces the risk of data breaches, thus balancing both regulatory compliance while offering confidentiality to the end user.

Federated Learning: This technique allows AI models to be trained on decentralized data, further protecting user confidentiality while still enabling effective content moderation.

4. Adaptability and Scalability:

Continuous Learning: AI models can continuously learn and adapt to new forms of harmful content and evolving user behavior. This ensures that the moderation system remains effective over time.

Scalability: AI-powered moderation can easily scale to handle the growing volume of content on BChat, ensuring a consistently safe and positive user experience. With more users, the decentralized data helps train the AI without human intervention.

By implementing confidential AI, BChat can:

Create a safer and more inclusive online community.
Improve the efficiency and accuracy of content moderation.
Enhance user confidentiality and trust.

Optimizing BelNet Through Confidential AI

BelNet integrates AI-driven route optimization to enhance the efficiency of data packet delivery across its decentralized VPN (dVPN) network. Using machine learning and AI algorithms, BelNet’s confidential AI optimizes the routing of data packets by considering factors such as node distance, network traffic, and load distribution. This approach ensures that data packets travel the most efficient routes, balancing the load across exit nodes to prevent congestion and maintain high performance.

By continuously learning from network conditions, AI route optimization adapts in real-time, selecting paths that reduce latency, save bandwidth, and enhance user experience. These optimizations also lead to energy savings, reducing the overall resource consumption of the network.

With BelNet AI’s confidential approach, users’ routing information remains encrypted and secure, ensuring that data security and confidentiality is never compromised while still benefiting from advanced AI optimizations.

Confidential Browsing with AI Summarization

The Beldex browser uses AI to summarise a website’s content by extracting the most important information. To summarise the text, the AI identifies key points and main connecting ideas. This gives users a quick overview of the content without reading the entire page. It helps users quickly grasp the content of lengthy articles or research papers.

Using AI and natural language processing (NLP), the browser structures and understands text, delivering concise summaries without sending data to external servers. Since the Beldex browser summarises locally without sending user data to external servers, it protects user confidentiality. Thus, Beldex prioritises data security, making the summarisation feature efficient and secure on the Beldex browser.

How FHE Supports AI at Scale in Beldex

Fully Homomorphic Encryption (FHE) allows computations on encrypted data, enabling scalable decentralized applications (dApps) while offering key advantages:

Data Security: FHE ensures dApps process sensitive data without exposing it, vital for applications dealing with financial transactions or personal information.
Offloading Tasks: FHE allows dApps to offload heavy computations to untrusted servers without compromising security, improving scalability and reducing network load.
Training on Encrypted Data: FHE enables privacy-preserving machine learning, allowing dApps to train and deploy models on encrypted data.
Verifying Computations: FHE can verify computations from untrusted sources, ensuring results’ integrity in decentralized apps.
Secure Data Sharing: FHE facilitates collaborative computations without compromising privacy, enabling efficient data sharing.

With FHE, dApps can perform complex operations securely, ensuring that even if infrastructure is compromised, the data remains protected.

Conclusion

Beldex’s decentralized confidential AI enhances both privacy and scalability. By integrating advanced technologies like FHE and confidential computing, Beldex enables AI computations on encrypted data without compromising user privacy. This ensures decentralized AI can learn from confidential data securely, driving smarter, safer, and more trustworthy applications across its ecosystem.