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Equivariant Encryption (EE) lets Nesa process encrypted data without ever decrypting it. ◽️You keep your data. ◽️The model stays private. ◽️The inference stays verifiable. That’s how privacy becomes performance. 🔲

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Nesa

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45,263 görüntüleme • 8 ay önce •via X (Twitter)

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🧵 Understanding Zama; the future of privacy tech & homomorphic encryption 1️⃣ Zama is pioneering fully homomorphic encryption (FHE). A breakthrough that lets you compute on encrypted data without decrypting it. 🔐 That means total privacy, even the system running your data can’t see it. 2️⃣ Why it matters: Right now, cloud apps, AI models, and databases must access your raw data to work. FHE changes that. your data stays private while still usable. 3️⃣ Zama builds open-source FHE tools for developers, turning advanced cryptography into practical products for AI, blockchain, and Web3. 4️⃣ Imagine: •AI that learns without reading your secrets 🤖 •Blockchain transactions with zero data leaks •Cloud apps that never see your info 5️⃣ Zama’s mission: Privacy should be the default, not an option. They’re making privacy-preserving tech simple, scalable, and open for everyone. 🔚 In a world obsessed with data, Zama might just be building the encryption layer of the future internet. 🌐
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🧵 Understanding Zama; the future of privacy tech & homomorphic encryption 1️⃣ Zama is pioneering fully homomorphic encryption (FHE). A breakthrough that lets you compute on encrypted data without decrypting it. 🔐 That means total privacy, even the system running your data can’t see it. 2️⃣ Why it matters: Right now, cloud apps, AI models, and databases must access your raw data to work. FHE changes that. your data stays private while still usable. 3️⃣ Zama builds open-source FHE tools for developers, turning advanced cryptography into practical products for AI, blockchain, and Web3. 4️⃣ Imagine: •AI that learns without reading your secrets 🤖 •Blockchain transactions with zero data leaks •Cloud apps that never see your info 5️⃣ Zama’s mission: Privacy should be the default, not an option. They’re making privacy-preserving tech simple, scalable, and open for everyone. 🔚 In a world obsessed with data, Zama might just be building the encryption layer of the future internet. 🌐

v͙e͙s͙p͙e͙r͙ 📊🐐

19,644 görüntüleme • 8 ay önce

You don’t need a data center to support private AI. ⬟ Private inference doesn’t live in one place. It runs across the network. If your device is online, it can contribute to privacy-preserving execution. Run a node on the Nesa network.
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You don’t need a data center to support private AI. ⬟ Private inference doesn’t live in one place. It runs across the network. If your device is online, it can contribute to privacy-preserving execution. Run a node on the Nesa network.

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28,966 görüntüleme • 6 ay önce

Health data shouldn’t be public. The Health app helps keep it private. Privacy. That’s iPhone.
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Health data shouldn’t be public. The Health app helps keep it private. Privacy. That’s iPhone.

Apple

38,628,063 görüntüleme • 3 yıl önce

Health data shouldn’t be public. The Health app helps keep it private. Privacy. That’s iPhone.
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Health data shouldn’t be public. The Health app helps keep it private. Privacy. That’s iPhone.

Apple

37,217,950 görüntüleme • 3 yıl önce

We do this for you. Your bandwidth becomes the fuel for AI. Your contributions enrich the data. Your checks keep it trusted. Your Vault grows in value. Let’s get you paid by the network. Trust the process ⚫️
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We do this for you. Your bandwidth becomes the fuel for AI. Your contributions enrich the data. Your checks keep it trusted. Your Vault grows in value. Let’s get you paid by the network. Trust the process ⚫️

Perceptron Network

79,905 görüntüleme • 10 ay önce

Your privacy stays safe. Your bandwidth creates value.⚫️ We’ll never touch your private data. No emails. No photos. No logins. Only the unused bandwidth you choose to share. Get started in 3 steps: 1⃣Download the extension → 2⃣Confirm your email 3⃣Log in, run quests, earn rewards
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Your privacy stays safe. Your bandwidth creates value.⚫️ We’ll never touch your private data. No emails. No photos. No logins. Only the unused bandwidth you choose to share. Get started in 3 steps: 1⃣Download the extension → 2⃣Confirm your email 3⃣Log in, run quests, earn rewards

Perceptron Network

59,609 görüntüleme • 9 ay önce

Funny how you can do the same thing with a local model without sending all your data to a remote cloud.
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Funny how you can do the same thing with a local model without sending all your data to a remote cloud.

Victor M

1,196,385 görüntüleme • 6 ay önce

The Dawn of a New Era on $SUI (9) Still in the festive spirit, let’s look at Tusky , Tusky is a storage service that's not controlled by one company. It uses something called WalrusProtocol to keep your data safe. Your data is encrypted from start to finish, so only you can see it. Instead of one place, your data goes to many different spots. This setup makes your data less likely to be lost or stolen. It helps keep your information safe and always available. Tusky gives you control over your files. You can easily manage them with the tools provided. You decide who gets to see your data. This makes it great for personal storage or working with others. It works with SuiNetwork for even more privacy. You can log in without sharing personal info, keeping everything more secure. This means only you can get to your data, with no third party involved. It is growing fast, with 100,000 uploads already. This indicates its increasing acceptance in the tech community focused on data sovereignty. It's good at managing lots of data safely. In tech, where you want to own your data, TuskyTools is popular. It gives users control over their information. This platform helps keep your data secure and gives you freedom.
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The Dawn of a New Era on $SUI (9) Still in the festive spirit, let’s look at Tusky , Tusky is a storage service that's not controlled by one company. It uses something called WalrusProtocol to keep your data safe. Your data is encrypted from start to finish, so only you can see it. Instead of one place, your data goes to many different spots. This setup makes your data less likely to be lost or stolen. It helps keep your information safe and always available. Tusky gives you control over your files. You can easily manage them with the tools provided. You decide who gets to see your data. This makes it great for personal storage or working with others. It works with SuiNetwork for even more privacy. You can log in without sharing personal info, keeping everything more secure. This means only you can get to your data, with no third party involved. It is growing fast, with 100,000 uploads already. This indicates its increasing acceptance in the tech community focused on data sovereignty. It's good at managing lots of data safely. In tech, where you want to own your data, TuskyTools is popular. It gives users control over their information. This platform helps keep your data secure and gives you freedom.

Kaboom.sui🐽 🌊,⛵

18,167 görüntüleme • 1 yıl önce

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!
0:15

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

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When data privacy matters most, Google Cloud and AMD deliver. Confidential computing on AMD with Google Cloud gives you high performance while helping safeguard your most sensitive data and intellectual property. Innovate with confidence at any scale. See how the most trusted companies trust AMD:
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When data privacy matters most, Google Cloud and AMD deliver. Confidential computing on AMD with Google Cloud gives you high performance while helping safeguard your most sensitive data and intellectual property. Innovate with confidence at any scale. See how the most trusted companies trust AMD:

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Introducing the Wynn Upgrade A privacy-first architecture for Hibachi, built with Celestia underneath and Succinct A high-performance CLOB with ZK proofs, the first to publish encrypted state on Celestia Private DA if we ever rug (we won’t), you can still get your funds out

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Why $Supra is Still Massively Undervalued 📜 Let’s break it down 👇 ◽️Solana ($107B MC) - A high-performance blockchain excelling in scalability ◽️Chainlink ($15.6B) - The go-to oracle network for connecting smart contracts with real-world data Both are giants in their respective fields. But what if one project could combine the speed and scalability of Solana with the real-world connectivity of Chainlink and then perform even better? ✅ Blazing fast speeds for seamless scalability - 500,000 TPS + MultiVM ✅ Advanced Native Oracles ✅ Native verifiable onchain randomness ✅ Automation with zero block delay ! ✅ Native cross-chain transactions Not to mention a big game changer for development and real world implementation with custom Supra Containers plug and play ecosystems 👨‍💻🛠️ Yet, Supra MC is only a fraction of that, despite its potential to replace both of these giants while delivering even more functionality ⚡️

BrotherHood

25,096 görüntüleme • 1 yıl önce

1/ ever shared a payslip, bank statement screenshot, or app screen just to prove something? that’s because your personal data is locked inside apps. Vouch helps you access that data — directly from the source — without exposing your account. imagine building an app that knows someone’s Uber income, Instagram followers, or Bank of America balance — without needing permission from those platforms. that’s what vouch enables.
0:15

1/ ever shared a payslip, bank statement screenshot, or app screen just to prove something? that’s because your personal data is locked inside apps. Vouch helps you access that data — directly from the source — without exposing your account. imagine building an app that knows someone’s Uber income, Instagram followers, or Bank of America balance — without needing permission from those platforms. that’s what vouch enables.

kleymelvin.base.eth .lvl🆙 #REVA HUDL (Ø,G) 🐝

160,743 görüntüleme • 1 yıl önce

A Guy paying $3,200/month to run AI agents in the cloud. Then he stacked 4 Mac Minis on his desk. Cost: • $2,396 one time • $12/month electricity Result: • Cloud bill: $3,200 → $0 • Faster response times • Private data stays local • $38,000+ saved in year one The craziest part? Once AI runs on your desk, every experiment becomes free. The cloud isn't always cheaper.
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A Guy paying $3,200/month to run AI agents in the cloud. Then he stacked 4 Mac Minis on his desk. Cost: • $2,396 one time • $12/month electricity Result: • Cloud bill: $3,200 → $0 • Faster response times • Private data stays local • $38,000+ saved in year one The craziest part? Once AI runs on your desk, every experiment becomes free. The cloud isn't always cheaper.

Radha Tripathi

40,773 görüntüleme • 1 ay önce

Modernization doesn’t look the same for every organization. That’s why Red Hat trusts AMD. Together, we’re building an open, flexible foundation that lets you scale workloads across data center, hybrid, and multi-cloud environments with consistency, performance, and control. See how the most trusted companies trust AMD:
0:15

Modernization doesn’t look the same for every organization. That’s why Red Hat trusts AMD. Together, we’re building an open, flexible foundation that lets you scale workloads across data center, hybrid, and multi-cloud environments with consistency, performance, and control. See how the most trusted companies trust AMD:

AMD

16,075 görüntüleme • 2 ay önce

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
0:11

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 görüntüleme • 4 ay önce

You can't serve your customer if you don't know your customer. I showed the team at Kreme & Krumbs how @Salesforce takes all that customer data and turns it into real insights. Now that’s how you scoop smarter. 🍦 #sponsored #TheFixer #TheFixeronFox
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You can't serve your customer if you don't know your customer. I showed the team at Kreme & Krumbs how @Salesforce takes all that customer data and turns it into real insights. Now that’s how you scoop smarter. 🍦 #sponsored #TheFixer #TheFixeronFox

Marcus Lemonis

15,465 görüntüleme • 11 ay önce

🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.
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🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.

Bernard Marr

10,980 görüntüleme • 10 ay önce

Can You Monetize Your Digital ID? 1. 📱 You hand over data to exist online 2. 💰 Corporations profit-you absorb risk 3. 🔐 Digital wallets flip the script 4. ✅ Verify identity WITHOUT revealing data 5. 💸 Get PAID for what you can prove 6. 🤖 AI deepfakes make old systems obsolete* 7. 🚀 Your identity becomes a deployable asset *that’s why ZK infrastructure like Billions is needed Full article by Billions CEO evin below 👇
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Can You Monetize Your Digital ID? 1. 📱 You hand over data to exist online 2. 💰 Corporations profit-you absorb risk 3. 🔐 Digital wallets flip the script 4. ✅ Verify identity WITHOUT revealing data 5. 💸 Get PAID for what you can prove 6. 🤖 AI deepfakes make old systems obsolete* 7. 🚀 Your identity becomes a deployable asset *that’s why ZK infrastructure like Billions is needed Full article by Billions CEO evin below 👇

Billions Network

56,727 görüntüleme • 6 ay önce

There's a reason you don't paste your bank statements into a chatbot. Now you don't have to think twice. Cluster x Phala: private inference on the models you already use, DeepSeek, Qwen, GLM, MiniMax, running sealed inside TEE hardware. Your prompt is invisible to everyone in the path. Including us. Every call returns a signed attestation, proof your data never left the enclave. You don't have to take our word for it. The hardware proves it. Full breakdown below.
0:18

There's a reason you don't paste your bank statements into a chatbot. Now you don't have to think twice. Cluster x Phala: private inference on the models you already use, DeepSeek, Qwen, GLM, MiniMax, running sealed inside TEE hardware. Your prompt is invisible to everyone in the path. Including us. Every call returns a signed attestation, proof your data never left the enclave. You don't have to take our word for it. The hardware proves it. Full breakdown below.

Cluster Protocol

41,320 görüntüleme • 1 ay önce