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Sunscreen @SunscreenTech

Add Fully Homomorphic Encryption (FHE) to your app in minutes, not months. sunscreen.tech encrypted Joined April 2021
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SunscreenTech Profile Picture

Sunscreen @SunscreenTech

3 weeks ago

Private internet voting has always had a hard constraint: How do you verify the tally without exposing the votes? In our latest post, we unpack why truly private voting remains elusive, and how ZKPs, threshold cryptography, and FHE get us closer. blog.sunscreen.tech/why-truly-priv…

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

3 months ago

Private information retrieval is the Trojan Horse of homomorphic encryption. It's already deployed to hundreds of millions of users by Apple and Meta. We broke down how it works and built a private DNS demo to show it in action: blog.sunscreen.tech/private-lookup…

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AxLabs @ax_labs

3 months ago

This is BIG news for FHE (Fully Homomorphic Encryption) adoption. spectrum.ieee.org/fhe-intel Tech being built by players like @SunscreenTech and @zama could one day be deployed at scale on specialized hardware like Heracles. Now imagine if companies like @nvidia eventually enter the space with chips designed for encrypted computing. 🤯 We’re still so early.

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SunscreenTech Profile Picture

Sunscreen @SunscreenTech

7 months ago

most people don't care about privacy, unless it enables a new technology or use case.

naval Profile Picture

Naval @naval

7 months ago

Say something true, in an interesting way.

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Charged Ventures @ChargedVentures

3 months ago

Web3 Privacy Ecosystem. Key Projects and Infrastructure As blockchain adoption grows, #privacy solutions are becoming essential for #finance, identity, and data #security. This overview maps the projects shaping the next generation of #crypto infrastructure.

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

3 months ago

FHE

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

6 months ago

x.com/i/article/1948…

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Sunscreen @SunscreenTech

3 months ago

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

3 months ago

The Achilles’ heel of software is its need to decrypt data for processing. FHE eliminates that window of exposure, enabling what some call “full-privacy computing." We are loving reading this from @barisozmen_twi Read: bozmen.io/fhe

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

3 months ago

FHE vs TEEs Read more: medium.com/@trekeptor/tee…

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Sunscreen @SunscreenTech

3 months ago

We're excited to announce our partnership with @latticaai to bring production-ready TFHE to developers. Our Parasol compiler helps builders turn ordinary code into high‑performance FHE programs. With Lattica's managed infrastructure, you can deploy encrypted computation without spinning up your own servers. What this means: compile locally with Sunscreen, deploy to Lattica, and run programs on encrypted data in production. No plaintext exposure, no infrastructure headaches. This opens the door for applications requiring exact computation on sensitive data - from smart contracts to secure voting to genomic analysis. Get started with 3 free compute hours: lattica.ai/tfhe Read more on our blog: blog.sunscreen.tech/deploying-tfhe…

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bitfalt @bitfalt

4 months ago

@fede_intern @zama @SunscreenTech really amazing team and extremely open-source oriented

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Lukas Helminger @luhelminger

4 months ago

@fede_intern @zama @fhenix @SunscreenTech

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SunscreenTech Profile Picture

Sunscreen @SunscreenTech

7 months ago

Privacy builders don't need to learn complex cryptography or rewrite their applications to integrate privacy anymore. SPF is the end to end, modular platform for building private software.

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Sunscreen @SunscreenTech

6 months ago

Private compute isn't just one thing, it's a spectrum of a trust model. TEEs trust hardware, FHE trusts math. Regardless of whether you're building an AI or blockchain app, you should ask: what (or who) are you trusting and whether that trust assumption works for your app.

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0x10d9e Profile Picture

lodge (priv/acc) @0x10d9e

6 months ago

Spot on. AI needs private inference YESTERDAY.

miguel_de_vega Profile Picture

Miguel de Vega @miguel_de_vega

6 months ago

Take ChatGPT for example. Everything you type: - can be used as evidence against you in court (there’s no legal confidentiality like you’d have with a therapist or lawyer): economictimes.indiatimes.com/tech/artificia… - can be used by default to train the model: openai.com/en-GB/policies… That’s

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Web3Privacy Now @web3privacy

6 months ago

Privacy is a universal internet language.

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

6 months ago

Both TEEs and FHE enable privacy-preserving computation, but they approach the problem from different angles. If performance is critical and trust in hardware is acceptable → TEE is a practical choice. If absolute data privacy and decentralization matter most → FHE is the holy grail.

SunscreenTech Profile Picture

Sunscreen @SunscreenTech

6 months ago

FHE lets you compute directly on encrypted data, without needing to decrypt data at all. Benefits: 1/ Trust in math; cryptographic guarantees 2/ Good for cloud or decentralized systems where zero trust is important 3/ When data is never decrypted, the attack surface is reduced significantly. Disadvantages: 1/ Currently slower than TEEs for many commercial use cases (but improving fast) 2/ Dev effort can be slightly higher.

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