This post explores the challenges of generating deterministic random numbers and using cosine within Nix expressions. It highlights that Nix's purity, while beneficial for reproducibility, makes tasks like generating unique identifiers difficult without resorting to external dependencies or impure functions. The author demonstrates various approaches, including using the derivation name as a seed for a pseudo-random number generator (PRNG) and leveraging builtins.currentTime as a less deterministic but readily available alternative. The post also delves into the lack of a built-in cosine function in Nix and presents workarounds, like writing a custom implementation or relying on a pre-built library, showcasing the trade-offs between self-sufficiency and convenience.
The blog post "Entropy Attacks" argues against blindly trusting entropy sources, particularly in cryptographic contexts. It emphasizes that measuring entropy based solely on observed outputs, like those from /dev/random, is insufficient for security. An attacker might manipulate or partially control the supposedly random source, leading to predictable outputs despite seemingly high entropy. The post uses the example of an attacker influencing the timing of network packets to illustrate how seemingly unpredictable data can still be exploited. It concludes by advocating for robust key-derivation functions and avoiding reliance on potentially compromised entropy sources, suggesting deterministic random bit generators (DRBGs) seeded with a high-quality initial seed as a preferable alternative.
The Hacker News comments discuss the practicality and effectiveness of entropy-reduction attacks, particularly in the context of Bernstein's blog post. Some users debate the real-world impact, pointing out that while theoretically interesting, such attacks often rely on unrealistic assumptions like attackers having precise timing information or access to specific hardware. Others highlight the importance of considering these attacks when designing security systems, emphasizing defense-in-depth strategies. Several comments delve into the technical details of entropy estimation and the challenges of accurately measuring it. A few users also mention specific examples of vulnerabilities related to insufficient entropy, like Debian's OpenSSL bug. The overall sentiment suggests that while these attacks aren't always easily exploitable, understanding and mitigating them is crucial for robust security.
The blog post "Fat Rand: How Many Lines Do You Need to Generate a Random Number?" explores the surprising complexity hidden within seemingly simple random number generation. It dissects the code behind Python's random.randint() function, revealing a multi-layered process involving system-level entropy sources, hashing, and bit manipulation to ultimately produce a seemingly simple random integer. The post highlights the extensive effort required to achieve statistically sound randomness, demonstrating that generating even a single random number relies on a significant amount of code and underlying system functionality. This complexity is necessary to ensure unpredictability and avoid biases, which are crucial for security, simulations, and various other applications.
Hacker News users discussed the surprising complexity of generating truly random numbers, agreeing with the article's premise. Some commenters highlighted the difficulty in seeding pseudo-random number generators (PRNGs) effectively, with suggestions like using /dev/random, hardware sources, or even mixing multiple sources. Others pointed out that the article focuses on uniformly distributed random numbers, and that generating other distributions introduces additional complexity. A few users mentioned specific use cases where simple PRNGs are sufficient, like games or simulations, while others emphasized the critical importance of robust randomness in cryptography and security. The discussion also touched upon the trade-offs between performance and security when choosing a random number generation method, and the value of having different "grades" of randomness for various applications.
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https://news.ycombinator.com/item?id=43669057
Hacker News users discussed the blog post about reproducible random number generation in Nix. Several commenters appreciated the clear explanation of the problem and the proposed solution using a cosine function to distribute builds across build machines. Some questioned the practicality and efficiency of the cosine approach, suggesting alternatives like hashing or simpler modulo operations, especially given potential performance implications and the inherent limitations of pseudo-random number generators. Others pointed out the complexities of truly distributed builds in Nix and the need to consider factors like caching and rebuild triggers. A few commenters expressed interest in exploring the cosine method further, acknowledging its novelty and potential benefits in certain scenarios. The discussion also touched upon the broader challenges of achieving determinism in build systems and the trade-offs involved.
The Hacker News post titled "RNG and Cosine in Nix" sparked a discussion with several interesting comments.
One commenter questioned the practicality of the approach, pointing out that using a hash function directly would likely be simpler and more efficient than the proposed cosine-based method. They also expressed concern about potential bias introduced by using cosine and suggested that a more rigorous statistical analysis would be necessary to validate the randomness quality.
Another commenter echoed this sentiment, emphasizing the importance of proper statistical testing for random number generation. They recommended using established test suites like TestU01 to thoroughly evaluate the randomness properties of the generated sequence.
One user focused on the security implications, warning that the proposed method might not be suitable for cryptographic purposes due to potential predictability. They advised against using custom RNG solutions in security-sensitive contexts and recommended relying on well-vetted cryptographic libraries instead.
A further commenter offered a different perspective, suggesting that the approach might be useful for generating deterministic random values based on a seed. They envisioned applications in procedural generation, where consistent results are desirable.
Another individual highlighted the importance of understanding the underlying distribution of the generated random numbers. They noted that different applications may require different distributions (uniform, normal, etc.) and that simply generating seemingly random numbers without considering the distribution could lead to incorrect results.
Several commenters discussed the mathematical properties of the cosine function and its suitability for RNG. Some expressed skepticism, while others defended its potential, albeit with the caveat that careful analysis and testing are crucial.
Finally, some comments touched on the specific use case within Nix, the package manager mentioned in the title. They speculated about the potential benefits and drawbacks of using this method for generating unique identifiers or other random values within the Nix ecosystem. However, no definitive conclusions were drawn regarding its practical application in Nix.