Fingerprinting nodes via addr requests

Randomizing timestamps by adding bounded noise, known as fuzzing, presents a method aimed at enhancing privacy and security. This technique involves introducing variability within a predefined range (±𝛿), aiming to obscure original data values. Despite its intent to complicate the direct linkage of data points, fuzzing does not ensure complete unlinkability. The inherent problem lies in the potential for attackers to de-obfuscate or "defuzz" these timestamps. Through sophisticated statistical methods such as maximum likelihood estimation (MLE), clustering, or Bayesian inference, skilled adversaries might successfully estimate pre-fuzzed values. This possibility raises concerns about whether two seemingly unrelated data points could be traced back to the same or similar origins.

A significant challenge with this approach is ensuring that new addresses do not appear anomalously old, which could inadvertently increase their risk of being flagged as suspicious or "Terrible." This outcome would counteract the protective measures intended by fuzzing. Moreover, the effectiveness of fuzzing is under scrutiny due to its inability to guarantee the unlinkability of data points. Despite the introduction of noise, correlations between original values might still be discernible, thereby compromising privacy.

The discussion invites further exploration into the practicality and efficacy of defuzzing techniques. Given the complexity of accurately reconstructing original values from fuzzed data, there's an open question about the realism of such attempts in operational environments. The conversation suggests a need for innovative solutions or enhancements to current methods to better protect data privacy and security in the face of evolving threats.