Practical failures of cryptographic key management rarely stem from weak algorithms: they arise from the difficulty users face in memorizing and reliably recalling high-entropy secrets. Password-based and brainwallet approaches collapse under selection bias, while machine-generated mnemonics such as BIP-39 impose a significant memory burden. This paper introduces GeoVault, a key derivation framework that uses remembered geographic locations as the cryptographic input. Keys are derived from a small set of user-selected map points, encoded deterministically using a geospatial scheme and hardened with the Argon2id memory-hard function. We develop a formal entropy model that distinguishes nominal from effective spatial entropy under attacker-prioritized geographic dictionaries and quantifies the additional reduction caused by demographic selection bias. Through information-theoretic analysis and CPU-GPU benchmarking, we show that spatial secrets carry a substantially higher effective entropy floor than human-chosen passwords, and that Argon2id creates a strong asymmetry between legitimate users and offline adversaries: at a memory cost of 1 GiB, an attacker using a high-end GPU can test approximately 66 candidate secrets per one defender key derivation. This residual throughput advantage is, however, overwhelmed by the exponential growth of the search space when multiple locations are selected. Selecting n≥3 geographic points is necessary and sufficient to achieve cryptographic-strength brute-force resistance under the global attacker prior across all evaluated Argon2id configurations. Against a demographically targeted attacker with city-level knowledge of the user, n=3 maintains Human-Scale Secure resistance; n=4 with a chaining depth of k=6 restores the Super Secure zone at an ≈8 s user-side wait. Single-point configurations remain insecure regardless of memory cost hardening.
Corn et al. (Wed,) studied this question.