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Confidential computing for high-assurance RISC-V embedded systems

Hacker News

Confidential computing for high-assurance RISC-V embedded systems

Dear HN community! Looking forward to hearing your feedback on ACE (assured confidential execution), technology that implements VM-based trusted execution environment (TEE) for embedded RISC-V systems with focus on a formally verified and auditable firmware. We target high-assurance systems that can benefit from compartmentalization and hardware-backed isolation. The key ingredient called security monitor (firmware) is implemented in Rust. The formal specification is defined as annotations directly in code and gets translated to Coq using RefinedRust automation. ACE design is now part of the RISCV confidential VM extension (CoVE) specification (deployment model 3).

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, using, code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

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