Let's consider possible options for compensating for the shortcomings of such random number generators.
You can come up with (invent) quite a few algorithms for improving the results of such data expanders.
- TTS conversion of the entered text several times with different parameters: sound quality, pronunciation speed, male/female voice, timbre, etc. With subsequent mixing of data from several sound files. The complexity of mixing algorithms directly affects the result. For example, you can "reverse" data in one or more audio files before mixing, introduce data shifts, block permutations, etc. At the same time, the repeatability of the results and the possibility of constructing synchronous generators of “random” numbers are preserved. To improve the quality of the result, it is necessary to evaluate entropy and use additional processing. One of the options readily available in Android is data compression - ZIP. A ZIP archive is created from the sound file, from which a section of compressed data is taken. According to a given algorithm, this data is mixed with the data of the previous stage. The specific mixing algorithm is selected after statistical evaluation of the data.
You can come up with (invent) quite a few algorithms for improving the results of such data expanders.
- TTS conversion of the entered text several times with different parameters: sound quality, pronunciation speed, male/female voice, timbre, etc. With subsequent mixing of data from several sound files. The complexity of mixing algorithms directly affects the result. For example, you can "reverse" data in one or more audio files before mixing, introduce data shifts, block permutations, etc. At the same time, the repeatability of the results and the possibility of constructing synchronous generators of “random” numbers are preserved. To improve the quality of the result, it is necessary to evaluate entropy and use additional processing. One of the options readily available in Android is data compression - ZIP. A ZIP archive is created from the sound file, from which a section of compressed data is taken. According to a given algorithm, this data is mixed with the data of the previous stage. The specific mixing algorithm is selected after statistical evaluation of the data.
Czech Entropy "random" number generator is now fully functional. Although the quality of entropy is not high, it can be significantly improved. In addition, it is very easy to generate numbers in a different dimension. At least 64 digit numbers. Variable length numbers can be easily generated. You can configure the generation of numbers with gaps in the generation range and even with floating gaps (spaces). Moreover, all this is synchronous.
Such generators must be programmed individually. The reliability of encryption greatly depends on this.
If you use a synchronous generator for encryption, there is a lot to do. There should be no more than two instances of the application. So that the application itself cannot be decoded, it is impossible to generate a large array of values and calculate the features of the program (this can be counteracted). The generator must use a neural network and train both instances of the application in the same way. The neural network will evaluate the quality of PRNG work and, based on the results, the generation algorithm will be rebuilt after each cycle (synchronously on both instances of the application)
Let's create such a neural network.
Such generators must be programmed individually. The reliability of encryption greatly depends on this.
If you use a synchronous generator for encryption, there is a lot to do. There should be no more than two instances of the application. So that the application itself cannot be decoded, it is impossible to generate a large array of values and calculate the features of the program (this can be counteracted). The generator must use a neural network and train both instances of the application in the same way. The neural network will evaluate the quality of PRNG work and, based on the results, the generation algorithm will be rebuilt after each cycle (synchronously on both instances of the application)
Let's create such a neural network.
PRNG
The addition of neural networks can achieve advanced performance PRNG applications. And change them as many times as you like, maintaining synchronicity of work.
public Double Prediction (Integer DataInput1, Integer DataInput2){
return neurons.get(5).compute(
neurons.get(4).compute(
neurons.get(2).compute(DataInput1, DataInput2),
neurons.get(1).compute(DataInput1, DataInput2)
),
neurons.get(3).compute(
neurons.get(1).compute(DataInput1, DataInput2),
neurons.get(0).compute(DataInput1, DataInput2)
)
);
}
The addition of neural networks can achieve advanced performance PRNG applications. And change them as many times as you like, maintaining synchronicity of work.
public Double Prediction (Integer DataInput1, Integer DataInput2){
return neurons.get(5).compute(
neurons.get(4).compute(
neurons.get(2).compute(DataInput1, DataInput2),
neurons.get(1).compute(DataInput1, DataInput2)
),
neurons.get(3).compute(
neurons.get(1).compute(DataInput1, DataInput2),
neurons.get(0).compute(DataInput1, DataInput2)
)
);
}
Software random number generators (synchronous generators) PRNG an interesting task for a programmer, engineer, mathematician. Mathematics here is of the highest level. We won't consider this for now. What can a software engineer do to improve the quality of the PRNG generator before a mathematician gets involved? To do this, you need to evaluate the quality of generating “random” numbers for repeatability, the presence of a pattern (N-th order), etc. And here there is an interesting opportunity. The repeatability of each algorithm used can be assessed if: there is access to a generator program or if there is a large array of generated data. Those. If your application generates a small array of “random” numbers, after which the generation algorithm changes, then it will be difficult to correctly assess the quality of the entropy and predict the result. https://rescuewebcam.blogspot.com/A new way to expand the original data with any coefficient has been invented. Allows you to convert the entered source data for PRNG into tens of gigabytes of data. The algorithm is formulaless and does not depend on the hardware (smartphone)
A second processing level, Mode2, has been added to the Czech Entropy APP (PRNG) Android application. It can be enabled by selecting the CheckBox at the bottom of the application screen. https://drive.google.com/file/d/1in6sX45yDGty7NyDGCskZhJYJ4wo8V_T/view?usp=sharing
Applications Czech Entropy and Czech Flow demonstrate the technical (software) possibility of creating such generators. There are many opportunities to improve the quality of the "random" numbers produced. Because Since these applications do not create real random numbers (sequences), it is impossible to use their public versions located in application stores or software archives for serious protection. You need to use custom versions with unpublished generation, sampling and improvement algorithms. However, now we will move on to the section of the document in which an algorithm (mechanism) will be proposed that can turn a public application into a custom one, improve the quality of random numbers and generate random numbers with an ideal distribution SYNCHRONOUSLY. https://docs.google.com/document/d/1KVdnGEbI1_Fc45BrVEa4XQeaRATy4ynjQg5Y9yWrBAo/edit?usp=sharingGoogle Docs
Synchronous random number generators PRNG
Synchronous random number generators PRNG Translation into English (Google Translate) Let's look at several ways to create software random number generators using external digital streams and without them. Let's write source code for Android Java - prototypes…
Czech Entropy APP ver 0.3 In version 0.3 of the Czech Entropy application, a second conversion level has been added that uses a different operating algorithm. You can enable it if you select CheckBox Mode 2 If the first level, Mode 1, uses extreme expansion of the source data, after which numbers are sampled (with constant coefficients for now), then the second level, Mode 2, performs entropy redistribution with data compression. Feedbacks have not yet been used. https://drive.google.com/file/d/1in6sX45yDGty7NyDGCskZhJYJ4wo8V_T/view?usp=sharing November 2023In version 0.35, Czech Encryption APP the MULTIMODE mode was added - in this case, the already generated “random numbers” are recorded as source data for the next stage of generating and sampling numbers. By pressing the GENERATE button you create a new array of numbers and so on as many times as you like. Moreover, these arrays of numbers are repeatable under the same conditions.
The Czech Entropy is a PRNG application for Android smartphones that can generate identical arrays of “random” numbers given the same initial data. In version 0.35, the MULTIMODE function was added - when the generated array of "random numbers" is used as input data to generate the next array of numbers. Arrays of numbers can be used for combined XOR encryption in Czech Encode application https://drive.google.com/file/d/1in6sX45yDGty7NyDGCskZhJYJ4wo8V_T/view?usp=sharingA module (method) is being developed for the Czech Entropy PRNG Android application to generate 8-bit “random” numbers. There will be two modes of operation of the function - with a constant value of 8 digits and generating numbers with the number of digits from 1 to 8. When developing sample applications for individual contracts (custom versions), a module (method) can be created to work with numbers with any number of digits. The only limitation will be the calculation time on the smartphone. The generated arrays of "random" numbers can be effectively used in another Android application - Czech Encoder APP, which uses mixed encryption. In the future, the Czech Entropy PRNG random number generator will use a neural network trained to search for and bypass patterns in the generated arrays of “random” numbers. This can make it much more difficult to crack encrypted messages. This will ensure synchronicity of the generated arrays of numbers on several smartphones. http://czechentropy.freetzi.com/ https://rescuewebcam.blogspot.com/Added EnCalibrate mode - entropy correction. Now we are testing the first method (from a whole set of methods) for manually setting parameters for data expansion, generating, transforming and retrieving numbers.Figure 1 shows an example of a negative result of the graphical test “distribution of random numbers on a plane”. There are obvious patterns. In Figure 2, the patterns are cut out - the “holes” mode is used. A special algorithm (for example, based on a neural network) identifies problematic areas of the data and excludes them from the output values. For consumers of random numbers, these "holes" simply do not exist or can be filled with values from arrays of numbers generated by other algorithms and having patterns in other areas. This approach can also be used for N-dimensional analysis.A second activity with online guidance has been added to the application. In the next version, the possibilities for manually selecting parameters for generating “random” numbers will be expanded.
Work continues on the mask module for transmitting data to the generator input (MCULTIMODE mode)
Download page: https://appsenjoy.com/en/ZsyGTTW0VHkiih9/file
Work continues on the mask module for transmitting data to the generator input (MCULTIMODE mode)
Download page: https://appsenjoy.com/en/ZsyGTTW0VHkiih9/file
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