entropy

信息熵 香农提出了“信息熵”的概念，解决了对信息的量化度量问题； 其描述信息源的不确定性 文章来源: Information Entropy

I've recently started a course on data compression at my university. However, I find the use of the term "entropy" as it applies to computer science rather ambiguous. As far as I can tell, it roughly translates to the "randomness" of a system or structure. What is the proper definition of computer science "entropy"? Entropy can mean different things: Computing In computing, entropy is the randomness collected by an operating system or application for use in cryptography or other uses that require random data. This randomness is often collected from hardware sources, either pre-existing ones

2019-10-24 20:16:59 来源： https://www.cnblogs.com/hyserendipity/p/11734602.html

So I am trying to store the symmetric key using DPAPI. All is well and great, but what to do with the entropy? This answered question here really doesn't provide enough insight. It seems like a slippery slope - I could use the machine store to store the entropy but then what prevents someone from getting at that as well? Note: I am storing the current key using the User Scope. So my question is - what is the best way to store the entropy using DPAPI? Anything you store locally can be compromised. But there are steps you can take to make it more difficult. There is a document on Handling

CryptGenRandom is a random number generator function in CryptoAPI in Windows. How much entropy has that random number generator ? I have already looked a lot, but I couldn't find it. Thanks in advance. The exact algorithm of Windows CryptGenRandom was never published, therefore, some security experts suggest not to use it at all. Some reverse-engineering and cryptanalysis was made. A published research ( Cryptanalysis of the Windows Random Number Generator - Leo Dorrendorf, 2007 ) examined Windows 2000 RNG and found a number of weaknesses in the design and implementation. The document also

How can I determine the statistical randomness of a binary string? Ergo, how can I code my own test, and return a single value that corresponds to the statistical randomness, a value between 0 and 1.0 (0 being not random, 1.0 being random)? The test would need to work on binary strings of any size. When you do it with pen and paper, you might explore strings like this: 0 (arbitrary randomness, the only other choice is 1) 00 (not random, its a repeat and matches the size) 01 (better, two different values) 010 (less random, palindrome) 011 (less random, more 1's, still acceptable) 0101 (less

I would like to calculate entropy of this example scheme http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html Can anybody please explain step by step with real values? I know there are unliminted number of formulas but i am really bad at understanding formulas :) For example in the given image, how to calculate purity is clearly and well explained The question is very clear. I need an example how to calculate entropy of this clustering scheme. It can be step by step explanation. It can be C# code or Phyton Code to calculate such scheme Here entropy formula I will

I've heard about people using light sensors, geiger counters, and other physical sensors to generate random numbers, but I'm skeptical. Is there really a way to generate random numbers from taking measurements of the physical world (using an Arduino or any other microcontroller)? If so, would these numbers ever be really random? to clarify: the question is about the feasibility of using microcontroller-gathered data to generate random numbers that could be applied soundly to cryptography-- an alternative to relying on a device's entropy. Taking analog "real world" measurements usually is a