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Fast, Clientless Oblivious DNS

Hacker News

Fast, Clientless Oblivious DNS

We built a new secure DNS service at dns.invisv.com or 35.244.200.159 -- give it a try and let us know what you think. "Clientless" in this context means it works on normal devices/browsers/OSes (without requiring special client software): it supports normal clients with DNS-over-HTTPS (DoH) -- at https://dns.invisv.com/dns-query -- and DNS-over-TLS (DoT), but adds the multi-hop privacy of Oblivious DNS. It’s an alpha service running only on the free tier at fly.io (with servers in three regions/locations in the US: Los Angeles, Dallas, Washington DC) through GCP and Fastly (using our Multi-Party Relay stack). Despite that, it seems to be as fast as other popular DNS services for users in many areas of the US, and, more importantly, we apply the decoupling principle to improve user privacy. The background here is that normal DNS queries are revealed to the DNS servers we use for everything we do online, and so we all rely upon the promises made by the providers of those DNS services to not log or use that data that they have about all our Internet usage. In prior research, we developed Oblivious DNS (ODNS), which tunneled encrypted DNS queries over DNS, thereby hiding the client's IP/identity from a second-hop DNS resolver that actually sees the unencrypted queries. (The ODNS approach was rolled out over HTTPS for Safari browsing by Apple in iCloud Private Relay.) This approach requires a custom DNS client. Instead, by tunneling DoH or DoT requests via our Multi-Party Relay (which uses the MASQUE protocol to tunnel requests through a third-party, in this case Fastly), we can enable ordinary DNS client stacks that only support plain DoH or DoT to get the benefits of ODNS. Eventually we'd like to launch this service at scale, in partnership with another organization, to provide a fast, global, privacy-preserving DNS service that everyone can benefit from.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: apple, user, new · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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