Tools for BTC Puzzle that uses GPU to speed up the process of finding a private key to a Bitcoin wallet using the Bloom filter algorithm

Ready-made scripts that use GPU to speed up the process of finding a private key to a Bitcoin wallet using the Bloom filter algorithm and the BTC Puzzle tool, since such a script does not exist in the provided search results. However, I can give you a general idea of how Python can be used to speed up cryptographic calculations using GPU and how to apply the principles of Bloom filter in theory.

Using GPUs to Accelerate Cryptographic Computing

To speed up cryptographic calculations on GPU, you can use libraries like PyOpenCL or CUDA. PyOpenCL allows you to work with OpenCL, which is supported by most modern GPUs.

Example of using PyOpenCL

pythonimport pyopencl as cl
import numpy as np

# Создание контекста и очереди
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)

# Пример ядра для вычислений
prg = cl.Program(ctx, """
    __kernel void example(__global float *data) {
        int idx = get_global_id(0);
        data[idx] *= 2;
    }
""").build()

# Данные для обработки
data = np.random.rand(1024).astype(np.float32)
buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)

# Выполнение ядра
prg.example(queue, data.shape, None, buf)

# Чтение результатов
result = np.empty_like(data)
cl.enqueue_copy(queue, result, buf)

print(result)

Bloom filter application

Bloom filter is a data structure that allows you to quickly check whether an element is present in a set. However, it is not directly suitable for finding private keys, since this is a task that requires an exact match, not a probabilistic one.

Theoretical application of Bloom filter

If you wanted to use a Bloom filter to filter out potential keys, you could store hashes of potential keys in it. However, this is ineffective for finding a specific private key, since a Bloom filter is not designed to recover the original data.

BTC Puzzle and GPU

To speed up the process of finding a private key using the BTC Puzzle tool, you can use GPU for parallel computing, but this will require adapting existing scripts to work with GPU.

To implement such a script you will need:

Learn PyOpenCL or CUDA to work with GPUs.
Adapt existing scripts to search for private keys for GPU.
Explore the possibility of using Bloom filter for your task, although it may not be effective for finding specific keys.

Unfortunately, without a specific “BTC Puzzle” code and more detailed information about your task, it is difficult to provide a ready-made solution.

How to Install and Configure PyOpenCL to Use GPU

Installing and configuring PyOpenCL to use a GPU involves several steps. Below is a step-by-step guide on how to configure PyOpenCL to work with a GPU.

Step 1: Installing the required packages

OpenCL Installation : To work with PyOpenCL, you need to have an OpenCL implementation installed. For NVIDIA video cards, OpenCL support is included in the CUDA SDK and the official driver. Intel and AMD also have their own SDKs for OpenCL 1 7 .
Installing PyOpenCL and NumPy :
- Use pip to install PyOpenCL and NumPy: bashpip install pyopencl numpy
- If you are using Anaconda, install via conda:bashconda install -c conda-forge pyopencl numpy

Step 2: Setting up the environment

Setting up environment variables : If you are using Linux, make sure that the environment variables for OpenCL are set correctly. For example, for the Intel OpenCL SDK, you may need to add the library paths to the LD_LIBRARY_PATH.
Checking if OpenCL works : Use the utility ocl-icdor examples from the CUDA SDK (for example, oclDeviceQuery) to check if OpenCL works on your device 1 .

Step 3: Example of using PyOpenCL

Once you have the environment installed and configured, you can test PyOpenCL with a simple example:

pythonimport pyopencl as cl
import numpy as np

# Создание контекста и очереди
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)

# Пример ядра для вычислений
prg = cl.Program(ctx, """
    __kernel void example(__global float *data) {
        int idx = get_global_id(0);
        data[idx] *= 2;
    }
""").build()

# Данные для обработки
data = np.random.rand(1024).astype(np.float32)
buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)

# Выполнение ядра
prg.example(queue, data.shape, None, buf)

# Чтение результатов
result = np.empty_like(data)
cl.enqueue_copy(queue, result, buf)

print(result)

Step 4: Troubleshooting

Eclipse and Anaconda issues : If you are using Eclipse with Anaconda, make sure the PyOpenCL versions match on the command line and in the Eclipse 2 environment .
Compilation errors : To debug compilation errors, you can enable compiler warnings by setting the environment variable PYOPENCL_COMPILER_OUTPUT=11 .

Conclusion

PyOpenCL enables efficient use of GPUs for parallel computing in Python. Proper environment setup and examples will help you get started with this library.

Which –global-ws and –local-ws options are best to use for best performance

--global-wsThe and parameters --local-wsare typically used in the context of virtualization or distributed systems to configure workspaces or clipboards. However, the provided search results do not provide information about specific values or recommendations for these parameters.

Typically, setting up workspaces or clipboards depends on the specific system or application you are using. For best performance, consider the following factors:

Buffer size : Increasing the buffer size can improve performance if the system frequently uses the buffer to temporarily store data. However, a buffer that is too large can result in inefficient use of memory.
Buffer Type : The choice between a global and a local buffer depends on the system architecture. A global buffer may be more efficient for shared resources, while a local buffer is better suited for isolated tasks.
System limitations : Consider operating system and hardware limitations. For example, available memory and network bandwidth may affect the choice of buffer size.
Performance Testing : Test with different settings to determine the optimal values for your particular task.

If you are using a specific application or system, it is recommended that you consult the documentation or forums associated with that application for more specific recommendations.

How to Use Bloom Filter to Speed Up Private Key Search

Using a Bloom filter to speed up private key searches can be effective if you are dealing with a large set of potential keys and want to quickly filter out those that are certain to not exist. However, because a Bloom filter is a probabilistic data structure, it can produce false positives, meaning that a key may be marked as existing when it actually does not.

How Bloom filter works

Initialization : A bit array of a specified size is created, with all bits initially set to 0.
Hashing : Each element (in this case, a potential private key) is passed through several hash functions, which produce indices into a bit array.
Setting Bits : The bits in these indices are set in 1.
Verification : When a new element is verified, it is also run through the same hash functions. If at least one of the resulting indices has a bit set to 0, the element definitely does not exist in the set.

Example implementation in Python

pythonimport mmh3
from bitarray import bitarray

class BloomFilter(object):
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = bitarray(size)
        self.bit_array.setall(0)

    def _hash(self, item, seed):
        return mmh3.hash(item, seed) % self.size

    def add(self, item):
        for seed in range(self.hash_count):
            result = self._hash(item, seed)
            self.bit_array[result] = 1

    def lookup(self, item):
        for seed in range(self.hash_count):
            result = self._hash(item, seed)
            if self.bit_array[result] == 0:
                return False
        return True

# Пример использования
bf = BloomFilter(500000, 7)  # Размер фильтра и количество хеш-функций

# Добавление элементов в фильтр
for key in potential_keys:
    bf.add(key)

# Проверка существования ключа
if bf.lookup(target_key):
    print("Ключ может существовать")
else:
    print("Ключ точно не существует")

Selecting the optimal parameters

Filter size ( m) : The larger the size, the lower the probability of false positives, but more memory is required. The formula for calculation is: m = -(n * ln(p)) / (ln(2))^2, where nis the expected number of elements, and pis the desired probability of false positives.
Number of hash functions ( k) : Optimal value: k = (m / n) * ln(2).

Important Notes

False positives : Bloom filter can produce false positives, so after filtering, you should check the keys with other methods.
Efficiency : Bloom filter is effective for quickly filtering out non-existent items, but is not suitable for precise searching.

Thus, Bloom filter can be useful for pre-filtering potential private keys, but requires additional checks to confirm the key’s existence.

How Bloom Filter Affects Data Security When Used in Private Keys

A bloom filter can affect data security when used in private keys in several ways, although its primary purpose is not to provide security but to optimize data searching and filtering.

Impact on safety

Privacy : Bloom filters do not store element values directly, which can be useful for sensitive data. However, since it is a probabilistic structure, it can produce false positives, which can lead to unnecessary checks or access to data that does not actually exist 2 3 .
Protection against dictionary attacks : In some cases, the Bloom filter can help protect against dictionary attacks because it allows you to quickly filter out non-existent keys without performing expensive operations to check each key 4 .
Vulnerability to false positives : The main weakness of the Bloom filter is the possibility of false positives. If an attacker knows that a certain key might be in the set (based on a false positive), he can try to use this key, which can lead to unnecessary security checks or even successful attacks if the system does not have additional security measures 5 6 .
Inability to delete : Since items cannot be deleted from a Bloom filter, this can lead to a situation where stale or compromised keys remain in the filter, potentially increasing the security risk 2 3 .

Conclusion

Bloom filter can be useful for optimizing search and filtering data, but it should not be used as a primary security measure. In the context of private keys, it is important to use additional security measures such as encryption and authentication to protect data from unauthorized access.

If you use a Bloom filter to filter potential private keys, make sure that:

Use optimal parameters : Adjust the filter size and number of hash functions to minimize the likelihood of false positives.
Add additional checks : After filtering, be sure to check keys using other methods to confirm their existence and validity.
Consider alternative data structures : If security is a priority, it may be worth using other data structures that provide accuracy and security, even if they are less efficient in terms of memory and speed.

What benefits does Bloom filter provide when used in private keys?

A bloom filter can provide several benefits when used in the context of private keys, although its primary purpose is not to provide security but to optimize data searching and filtering.

Benefits of Using Bloom Filter

Memory savings : The Bloom filter requires significantly less memory than storing the full data set. This can be useful for large collections of private keys, where storing each key explicitly may be impractical 4 7 .
Speeding up search : The insertion and verification operations in Bloom filter are constant time, making it efficient for quickly determining whether a key is present in a set or not. This can speed up the process of filtering out non-existent keys 6 8 .
Query optimization : In the context of databases or distributed systems, a Bloom filter can help optimize queries, especially when multiple conditions need to be checked at once. This can be useful for filtering keys based on specific attributes 1 3 .
Reduced Query Rate : Using a Bloom filter can reduce the number of queries for non-existent data, which can be especially useful in systems with expensive read operations or network requests 2 4 .

Limitations and Considerations

False Positives : The main drawback of the Bloom filter is the possibility of false positives. This means that the filter may report the presence of a key that does not actually exist. Therefore, after filtering, additional checks must be performed to confirm the existence of the key 4 7 .
Inability to delete : The classic Bloom filter does not support deleting elements, which can be a problem in dynamic systems. Modified versions such as Counting Bloom filters 1 4 exist to solve this problem .

Overall, a Bloom filter can be useful for pre-filtering private keys, but it should not be used as the only security measure. It is important to additionally check keys and use other security measures to protect data.

What are the modified versions of Bloom filters for private keys?

There are several modified versions of Bloom filters that may be useful for working with private keys or similar data:

1. Counting Bloom filters

This modification uses counters instead of a simple bit array. Each counter allows tracking the addition and removal of elements, which solves the problem of the inability to remove in the classic Bloom filter 1 2 3 .

2. Sorted Bloom Filters

Although not widely used, sorted Bloom filters can be useful for optimizing queries where the order of elements is important.

3. Parallel Bloom Filters

Can be implemented on GPUs or distributed systems to speed up processing of large data sets. Use technologies such as CUDA for parallel computing 3 .

4. Encrypted Bloom Filters

Can be used to protect sensitive data. For example, in Parquet files, Bloom filters can be encrypted using the AES GCM 4 cipher .

5. Dynamic Bloom Filters

Designed for dynamic data sets where elements are frequently added or removed. May use a combination of different hash functions and methods to minimize false positives.

Application in the context of private keys

When using modified Bloom filters for private keys, it is important to consider the following factors:

Security : Use encrypted versions of filters to protect sensitive data.
Efficiency : Choose a filter that is optimized for your specific needs, such as parallel processing or dynamic updating.
Accuracy : Since Bloom filters can produce false positives, be sure to verify the results with other methods.

In general, the choice of a modified version of the Bloom filter depends on the specific requirements of your system and the characteristics of the data you are working with.

Citations:

Source code

Telegram: https://t.me/cryptodeeptech

Video: https://youtu.be/i9KYih_ffr8

Video tutorial: https://dzen.ru/video/watch/6784be61b09e46422395c236

Source: https://cryptodeeptech.ru/discrete-logarithm

Useful information for enthusiasts:

[1] YouTube Channel CryptoDeepTech
[2] Telegram Channel CryptoDeepTech
[3] GitHub Repositories CryptoDeepTools
[4] Telegram: ExploitDarlenePRO
[5] YouTube Channel ExploitDarlenePRO
[6] GitHub Repositories Keyhunters
[7] Telegram: Bitcoin ChatGPT
[8] YouTube Channel BitcoinChatGPT
[9] Bitcoin Core Wallet Vulnerability
[10] BTC PAYS DOCKEYHUNT
[11] DOCKEYHUNT
[12] Telegram: DocKeyHunt
[13] ExploitDarlenePRO.com
[14] DUST ATTACK
[15] Vulnerable Bitcoin Wallets
[16] ATTACKSAFE SOFTWARE
[17] LATTICE ATTACK
[18] RangeNonce
[19] BitcoinWhosWho
[20] Bitcoin Wallet by Coinbin
[21] POLYNONCE ATTACK
[22] Cold Wallet Vulnerability
[23] Trezor Hardware Wallet Vulnerability
[24] Exodus Wallet Vulnerability
[25] BITCOIN DOCKEYHUNT

How to extract RSZ values (R, S, Z) from Bitcoin transaction RawTx and uses the "Bloom filter" algorithm to speed up the work

Using GPUs to Accelerate Cryptographic Computing

Example of using PyOpenCL

Bloom filter application

Theoretical application of Bloom filter

BTC Puzzle and GPU

How to Install and Configure PyOpenCL to Use GPU

Step 1: Installing the required packages

Step 2: Setting up the environment

Step 3: Example of using PyOpenCL

Step 4: Troubleshooting

Conclusion

Which –global-ws and –local-ws options are best to use for best performance

How to Use Bloom Filter to Speed ​​Up Private Key Search

How Bloom filter works

Example implementation in Python

Selecting the optimal parameters

Important Notes

How Bloom Filter Affects Data Security When Used in Private Keys

Impact on safety

Conclusion

What benefits does Bloom filter provide when used in private keys?

Benefits of Using Bloom Filter

Limitations and Considerations

What are the modified versions of Bloom filters for private keys?

1. Counting Bloom filters

2. Sorted Bloom Filters

3. Parallel Bloom Filters

4. Encrypted Bloom Filters

5. Dynamic Bloom Filters

Application in the context of private keys

Citations:

Useful information for enthusiasts:

How to Use Bloom Filter to Speed Up Private Key Search