3 ms·

> More fundamentally: is relying on long-term human memory as a cryptographic reconstruction mechanism inherently flawed? I'm more worry about this. If the que

> More fundamentally: is relying on long-term human memory as a cryptographic reconstruction mechanism inherently flawed? I'm more worry about this. If the question is about your mothers name, the answer is probably in Facebook. If the question is your favorite 5 ice cream flavors, I'd probably change my mind assuming I didn't lie to avoid giving an easy answer and now I have no idea what I made up 10 years ago.

YuriiDev 8mo ago
That is a very valid concern and the main reason why "Cognitive Entropy" is tricky. To mitigate this, I’ve focused on three layers of defense: Static Facts vs. Subjective Tastes: I advise users to avoid "dynamic" memories (like favorite flavors) in favor of "static" facts that are etched into long-term memory but aren't easily searchable (e.g., specific digits from an old, expired ID, or the exact layout of a childhood home). LLM-Assisted Question Grading: The app includes a prototype tool (integrated with an LLM) that helps users evaluate their questions. It "grades" them based on two factors: Memorability (will you remember this in 10 years?) and Guessability (can this be found on Facebook/OSINT?). If a user picks "Mother's maiden name," the system flags it as high-risk. The "Physical Anchor" Defense: This is crucial. Even if an attacker knows your mother's name, they cannot even see the question or attempt the Argon2 cascade without the initial seed (k_0) derived from your "Physical Anchor" (the file hash). The answers are useless without the specific photo or document you chose as a seed. Encrypted Hints: The system allows embedding hints directly into the questions. Since the questions themselves are encrypted, these hints are only revealed step-by-step to the person who already unlocked the previous layer. I’ve detailed the philosophy behind this "Cognitive Security" in my White Paper Vol. 1 — Vision & Concept: https://secretmemorylocker.com/white-paper/en/vision-and-concept https://secretmemorylocker.com/white-paper/en/vision-and-con...

YuriiDev 8mo ago

That is a very valid concern and the main reason why "Cognitive Entropy" is tricky. To mitigate this, I’ve focused on three layers of defense: Static Facts vs. Subjective Tastes: I advise users to avoid "dynamic" memories (like favorite flavors) in favor of "static" facts that are etched into long-term memory but aren't easily searchable (e.g., specific digits from an old, expired ID, or the exact layout of a childhood home). LLM-Assisted Question Grading: The app includes a prototype tool (integrated with an LLM) that helps users evaluate their questions. It "grades" them based on two factors: Memorability (will you remember this in 10 years?) and Guessability (can this be found on Facebook/OSINT?). If a user picks "Mother's maiden name," the system flags it as high-risk. The "Physical Anchor" Defense: This is crucial. Even if an attacker knows your mother's name, they cannot even see the question or attempt the Argon2 cascade without the initial seed (k_0) derived from your "Physical Anchor" (the file hash). The answers are useless without the specific photo or document you chose as a seed. Encrypted Hints: The system allows embedding hints directly into the questions. Since the questions themselves are encrypted, these hints are only revealed step-by-step to the person who already unlocked the previous layer. I’ve detailed the philosophy behind this "Cognitive Security" in my White Paper Vol. 1 — Vision & Concept: https://secretmemorylocker.com/white-paper/en/vision-and-concept https://secretmemorylocker.com/white-paper/en/vision-and-con...