The Collatz Hypothesis is known as the 3n+1 problem or Hayes problem and remains an open problem in number theory.

The hypothesis reveals the difficulty of mathematical explanation of computation in “dynamical systems”. At present, scientists come to a partial solution of the problem. [1] The use of series, interpolation and number systems other than decimal allows us to approach the solution of the hypothesis.

The complexity of this hypothesis is determined by only one condition and two elementary functions. Expanding the number of conditions of the hypothesis with even more conditions and functions (for example, 256) complicates the description of the “dynamic system” exponentially.

Or in general terms, one iteration of a key can be defined:

Where is the field of natural numbers and the field of algebraic functions . Let x be a variable belonging to the field .

In addition, the Collatz conjecture has the property “inexpressibility in elementary functions”. This is due to the irreversible function n=n/2. The use of “expressible functions” improves the fillability of the range of numbers.

The hypothesis has the property of convergence of series of natural numbers in a finite iterative process. If we limit the number of iterations, the property of convergence of series of natural numbers is lost.

The Collatz hypothesis considers the inverse function for the “threshold function”, which characterizes its unpredictability and computational complexity.

Extension of the function considered in the Collatz hypothesis by means of iterations with changing conditions at each iteration allows the algorithm to work not only in the field of natural numbers, but also in the field of functions.

The algorithm applying irreversible functions gives a stochastic distribution of numbers and can be used to change private keys.

The algorithm applying reversible functions gives a normal distribution:

and can be used to compute public-private key pairs.

The algorithm is modified using transformations that produce different results in different number systems (number system base). For example, rearrangement or replacement of digits in a number will have a different solution in different number systems. This part of the algorithm provides calculations different from binary number systems.

This is a feature of the NaVeOl method and according to the authors – increases the complexity of the program as a whole.

__https://www.cecm.sfu.ca/organics/papers/lagarias/paper/html/paper.html__

Our system represents a departure from established paradigms, positioning itself as a transformative process rather than adhering to conventional notions of encryption. Instead of following traditional encryption methods, it undergoes a series of transformations and personalizes diverse sets of data. While we simplify it by labeling it as encryption for ease of understanding, the methodology originates from our flagship product—a data compressor. This compressor operates on the principle of digital, bit-format molecular genomic cell division.

The NaVeOl_Eco 8(to)7 method is specifically crafted for the local and peer-to-peer storage and utilization of data. It diverges from the use of asymmetric algorithms or public and private key pairs.

Its mathematical intricacy is characterized by a variable algorithm, defying expression through a deterministic mathematical formula. The algorithm is dynamically generated, relying on the input key. The update of round keys involves both irreversible and reversible functions

software, is grounded in scientific principles, has undergone 12 years of rigorous testing and development, demonstrated flawless performance without a single failure, operates excellently, is entirely devoid of vulnerabilities (as per testing), has no backdoors, surpasses AES 256 in speed by more than twice, operates without a hardware chip, and exhibits an entropy approaching 8 (7.999621) on any data without the use of round keys.

Our foundation rests on having conducted mathematical calculations. ( a journey of more than 12 year)

The intricacy involved in encrypting the data, coupled with transforming it into an encryption-plus-ecosystem, validates our assertion that this qualifies as a MOLECULAR-GENETIC CRYPTOGRAPHY” (post quantum encryption)., we are establishing our own standards that are anticipated to be acknowledged in the nearby future. To ensure quantum computing resistance, our standards will surpass others.(current)

Numerous requests prompted us to outline the distinctions and similarities between 8to7 Post-Quantum encryption and Falcon encryption, as well as CRYSTALS-Kyber encryption.

We can unequivocally state that there is no comparison; our approach in post-quantum encryption introduces something entirely novel. We refrain from merely stacking existing encryptions or adhering to the traditional Bob and Alice principles. Upon delving into our material, you will likely be convinced that both existing and new encryptions fade into insignificance when compared to the robustness of our ecosystem.

8(to)7 will be the new standard in Post quantum Encryption, we have no doubt about that

**Let’s start with a brief overview on:**

**Falcon encryption, as well as CRYSTALS-Kyber encryption.**

The encoding speed of the Falcon encryption system depends on several factors, including the size of the data block, the length of the key, and the algorithm used. Overall, Falcon’s encoding speed is about 200 Mbps for a 128-bit data block and a 128-bit key. This means Falcon can encrypt 200 million bits of data per second.

Here are more details about Falcon encoding speed for various parameters:

Data Block Size: Falcon encoding speed increases as the data block size increases. For example, for a 256-bit data block, Falcon’s encoding speed is about 400 Mbps.

Key Length: Falcon’s coding speed decreases as key length increases. For example, for a 256-bit key, Falcon’s encoding speed is about 150 Mbps.

Algorithm used: Falcon encoding speed also depends on the algorithm used. For example, the Falcon-512 algorithm is faster than the Falcon-128 algorithm.

For example, for a data block of 128 bits and a key of 128 bits, Falcon’s encoding speed is about 200 Mbps. This means Falcon can encrypt 200 million bits of data per second. This is fast enough for most applications, including network traffic encryption, disk encryption, and file encryption.

It is important to note that Falcon encoding speed may vary depending on the specific implementation. For example, some Falcon implementations may be optimized to use certain hardware platforms.

The encoding speed of the CRYSTALS-Kyber encryption system depends on several factors, including the size of the data block, the length of the key, and the implementation used. Overall, the CRYSTALS-Kyber encoding speed is about 1.5 Gbps for a 128-bit data block and a 256-bit key. This means that CRYSTALS-Kyber can encrypt 1.5 billion bits of data per second.

Here are more details about CRYSTALS-Kyber encoding speed for various parameters:

Data Block Size: CRYSTALS-Kyber encoding speed increases as the data block size increases. For example, for a 256-bit data block, the CRYSTALS-Kyber encoding speed is about 3 Gbps.

Key Length: CRYSTALS-Kyber’s encoding speed decreases as the key length increases. For example, for a 512-bit key, the CRYSTALS-Kyber encoding speed is about 1 Gbit/s.

Implementation used: The encoding speed of CRYSTALS-Kyber also depends on the implementation used. For example, some implementations of CRYSTALS-Kyber may be optimized for use on certain hardware platforms.

For example, for a data block of 128 bits and a key length of 256 bits, the CRYSTALS-Kyber encoding speed is about 1.5 Gbps. This means that CRYSTALS-Kyber can encrypt 1.5 billion bits of data per second. This is fast enough for most applications, including network traffic encryption, disk encryption, and file encryption.

It is important to note that CRYSTALS-Kyber encoding speed may vary depending on the specific implementation. For example, some implementations of CRYSTALS-Kyber may be optimized for use on certain hardware platforms.

**Our encryption system offers distinct advantages when compared to others:**

Altering a single bit results in a comprehensive transformation of the entire file.

Modifying a single bit leads to a complete alteration in the ENTIRE encoding ALGORITHM, where the conversion functions undergo ABSOLUTE changes.

The coding complexity is solely dependent on computational power, and even with the integration of quantum computing and ultra-fast AI, updating the outdated version (without complete decoding) with a more modern one (even without altering the algorithm, but by adding rounds) is sufficient. This ensures that the file’s cryptographic protection is consistently updated.

The key’s multifactorial nature allows the incorporation of varied information (diverse in type and number of individual keys, and variable in key length) and media sources, complicating key selection.

Speed stands out as a key advantage of this technology. Instead of relying on a small number of intricate and slow functions, we leverage a large number of simpler and faster functions. This approach enhances encryption strength (increasing the number of changes per unit of data) and facilitates algorithm adaptation to even the most basic microcontrollers, enabling the technology to be universally applied.

Here are several primary advantages, accompanied by additional benefits that markedly differ from contemporary encryption methods. In our scenario, we establish a unique ecosystem with its proprietary format, rendering it inaccessible to anyone other than the owner.

The average speed of processing data stands at 240-280 megabytes per second with a 512-byte key. The minimum key size is 4,096 bits.

For enthusiasts of our algorithm, here’s a partial breakdown of the code:

CheckFormAndMakeKey5: This function checks if the key form is visible and, if so, hides it. It also generates a 256-bit key Key5 from the key form data.

EcoIn: This function encrypts a file and saves it to a new file. It reads the file in blocks orrent byte of the key, and the current byte of the transformation matrix MAT.

FreshQMAT: This function modifies the transformation matrix MAT based on the current block index. The specific operation depends on the value of the varSeed parameter.

Encoding_IQ2: This function encrypts a block of data using the Key5 and MAT. It first XORs each byte of the data with the corresponding byte of the key. Then, it applies a transformation based on the transformation matrix MAT.

The overall encryption process involves generating a 256-bit key, using it to encrypt blocks of data, and applying a transformation matrix to the encrypted data. The exact details of the encryption algorithm are not clear from the provided code, but it appears to be a combination of XOR, rotation, and addition operations.f 256 bytes, encrypts each block using the Encoding_IQ2 function, and writes the encrypted block to the new file.

FreshQQKey: This function modifies the key Key5 by adding the current block index, the cu

Mathematical formula

The mathematical formula for this code is as follows:

E(d) = XOR(d, K) * MAT

where:

E(d) is the encrypted value

d is the original data

K is the key

MAT is the transformation matrix

How the algorithm works

** **

**The algorithm works as follows:**

A 256-bit key K is generated.

The original data d is split into blocks of 256 bytes.

Each block of data is encrypted using the following formula:

b’ = XOR(b, K) * MAT

where:

b is the original data block

b’ is the encrypted data block

The encrypted data blocks are written to a new file.

More details about each step

Key generation

The key K is generated from the data entered by the user in the form. The form data is converted to a 256-bit binary code, which is then used as the key.

Data splitting into blocks

The original data is split into blocks of 256 bytes. This is done to simplify the encryption process.

Data encryption

Each data block is encrypted using the formula XOR(b, K) * MAT.

The first operation XOR(b, K) is an exclusive OR operation. It performs a modulo 2 addition operation between each byte of the original data block b and the corresponding byte of the key K.

The second operation * MAT is a matrix multiplication operation. It performs a transformation on the encrypted data block b’ using the transformation matrix MAT.

Writing encrypted data

The encrypted data blocks are written to a new file.

Algorithm evaluation

The algorithm is sufficiently complex, making it difficult to break.

**Example of how the formula works:**

Let’s assume we have the original data d as the following string:

“This is an example of encrypted data.”

This data is 46 bytes long.

Now, suppose we have the key K as the following line:

“abcdefghijklmnopqrstuvwxyz”

This key is 26 bytes long.

Before we begin encryption, we must break the original data into blocks of 256 bytes. In our case, this will result in the following blocks:

b1 = “This is an example”

b2 = “mple of encry”

b3 = “pted data.”

We can then encrypt each block of data using the formula XOR(b, K) * MAT.

For block b1, the XOR(b, K) operation will give the following result:

b1′ = “This is an exa” ^ “abcdefghijklmnopqrstuvwxyz”

This will result in the following line:

“This is an exac”

Now we can use the transformation matrix MAT to transform the encrypted data block b1′. In our case, the MAT matrix looks like this:

MAT = [

[1, 2, 3, 4],

[5, 6, 7, 8],

[9, 10, 11, 12],

[13, 14, 15, 16]

]

The *MAT operation will give the following result:

b1” = b1′ * MAT

This will result in the following line:

“This is an exac”

Now we can repeat this process for block b2 and block b3. As a result, we will receive the following encrypted data:

b2′ = “mple of encr”

b3′ = “ypt”

Finally, we can write the encrypted data to a new file. In our case this will result in the following file:

This is an exac

mple of encr

ypt

To decrypt this data, we can use the same formula, but with the order of operations reversed. We first multiply the encrypted data by the MAT transformation matrix. We then perform an exclusive or XOR operation between the encrypted data and the key K.

As a result, we will receive the original data d in its original form.

Here’s a more detailed explanation of each step:

Key generation

Key K can be generated in any way. In our example, we simply used a character string. However, to improve security, the key K must be random and at least 256 bits long.

Breaking data into blocks

Dividing the data into blocks of 256 bytes is done in order to simplify the encryption process. This is because XOR and *MAT operations can be performed more efficiently on data blocks of this size.

Encryption of data blocks

The XOR(b, K) operation is an exclusive or operation. It performs a modulo-2 addition operation between each byte of the original data block b and the corresponding byte of the key K.

As a result of the XOR(b, K) operation, each byte of the original data block will be encrypted with a unique value.

The * MAT operation is a matrix multiplication operation. It transforms the encrypted data block b’ using the transformation matrix MAT.

The MAT matrix can be used to perform various transformations on an encrypted block of data. In our example, the MAT matrix simply rotates each byte of the encrypted data block by one bit.

Recording encrypted data

Encrypted data can be written to any file. In our example, we simply wrote the encrypted data to a new file.

**Now lets go deeper and deeper into 8(to)7**

The focus is on the integration of encryption and compression, where the coding principle for establishing an ecosystem relies on our compressor methodology. The core concept is rooted in our archiver, with 50% of the platform directly aligned with its operational principles. Retrieving information becomes significantly challenging if it is missing.

Consider our encryption system as a distinct format tailored for each user, functioning as an exclusive ecosystem. Only the owner of this ecosystem possesses the capability to pre-code it, rendering alternative options practically unattainable. While we can substantiate the benefits, it’s important to note that this is just a fraction of what our system offers.

Point 1 encapsulates a fundamental tenet of Hashing cryptographic primitives: altering just one bit disrupts the entire symphony, as illustrated in “Alien: Covenant.” This concept is integral to our approach within the Cyfers class.

However, our paramount advantage lies in a multi-factor key and a comprehensive six-factor verification process for every 256-byte block. This marks the advent of a new era in coding, unlike anything conceived before; our system stands as an impeccable innovation.

Our system seamlessly encrypts and decrypts all data without ever acknowledging an incorrect password. It operates without backdoors and is entirely devoid of flaws, setting a new standard in cryptographic reliability.

**High-entropy data archiving**

Home encoding is a method of encoding (compressing) permutation sequences using the inverse probability of encountering an expected value. Reducing or nullifying the probability of encountering a specific number in a permutation sequence leads to shifting the value to the end of the dictionary table or excluding that number from the dictionary table. By using an index from the remainder of the sorted dictionary, it is possible to replace the value with an index from the MAT table described by a number of lower bit depth. The last number in the compressed array is not specified.

For example, encoding a two-bit sequence.

Input data 0 3 2 1 4 x 2 = 8 bits

Compressed data 0 3 1 2 + 2 + 1 = 5 bits

Compressed data 1 0 1 2 + 2 + 1 = 5 bits

Restored data 1 0 3 2 4 x 2 = 8 bits

Encoding a three-bit sequence.

Input data 0 1 2 3 4 5 6 7 8 x 3 = 24 bits

Compressed data 0 1 2 3 0 1 0 3 + 3 + 3 + 3 + 2 + 2 + 1 = 17 bits

Compressed data 7 5 2 1 3 0 1 3 + 3 + 3 + 3 + 2 + 2 + 1 = 17 bits

Restored data 7 5 2 1 6 0 4 3 8 x 3 = 24 bits

Class encoding.

The class of sets – a set of data arrays described by the same length of the data array – L=const, and the same number of unique numbers in the array – U=const.

For example, for a four-bit sequence:

0 1 2 3 9 5 6 7 8 9 9 11 1 13 14 0 L = 16, U = 10 (Subset 10)

Subset of sets – a set of data arrays that belong to the same Set class, but differ in the number of unique numbers UR=const.

For example, for a four-bit sequence:

0 1 2 3 9 5 6 7 8 9 9 11 1 13 14 0 UR = 3; (Subset 3)

**Compression of a set class.**

By assigning user data to a specific set class, the algorithm enables the exclusion of other set classes. This observation indicates the existence of structural features in the data, opening the possibility of compressing data size while considering these features.

Efficiency dictates the use of the Home coding algorithm when the values of L and U are closely matched. Conversely, if the values of L and U significantly differ, the algorithm involving the replacement of an array with its ordinal number in a sorted class set becomes more effective.

In our context, the password assumes a wholly secondary role; an individual can utilize their usual password, yet it remains impervious to decryption due to numerous additional factors known only to the data owner. This is a multifactorial approach.

Our minimum key spans 4,096 bits, requiring the entire mathematical universe to select the correct keys, amounting to 256 to the 256th power. The impossibility of a quantum theoretical brute force is inherent in this key.

The foundation of our 8to7 ecosystem is built upon ID, serial number, license, along with a structural file, program version, and password.

**Compression Efficiency and Data Integrity**

The algorithm described above allows compressing 256 evenly distributed bytes into a block of 256 bytes, resulting in a compression rate of -12.5%. This compression technique does not entail any metadata and ensures no data loss.

**Modified Compression Principles**

While we have partially adopted compression principles, we have introduced new modifications to enhance chaos in a reversible manner, without requiring additional data. Our approach takes a completely different direction, focusing on maximizing chaos and achieving reversibility. Demonstrating it in a live demonstration would enable a comprehensive understanding of our method.

The byte system transforms into a distinct structure, forming a separate object associated with two MAT tables. Each table consists of associations between 128 bytes and a seven-bit transformation for decoding, providing sufficient power for encryption.

A single non-data bit functions as a switch for switching between the tables. This bit is absorbed as a valid bit in a byte, rendering the structure destructible in any possible combination.

For numerous companies and institutes worldwide, it remains an unexplored field, but not for 8(to)7. We possess the capability to achieve lossless compression of White Noise up to 12.5% using our proprietary compression methodology and integrated compressor.

**White Noise Compression**

Based on the working principle of our algorithm, we have developed a white noise compression technique. The byte movement within the block of 256 bytes creates an environment where the order of bytes becomes irrelevant. This results in a structure resembling white noise patterns. By harnessing this unique property, we can compress data using white noise patterns. This highlights the effectiveness and power of our encryption system.

**Maximum Entropy and Byte Distribution**

In our system, we work with the highest entropy, achieved through the even distribution of byte blocks across a field of 256 bytes, without any repetitions. This maximum entropy ensures the robustness and security of our encryption system.

In conclusion, we have presented an innovative encryption system that utilizes chaotic byte movement and white noise compression. Our system allows for arbitrary byte movement within a 256-byte block, without the need for a specific order or addressing. By ensuring no duplicate bytes, we create a dynamic environment that maximizes entropy.

While partially incorporating compression principles, we have introduced modifications to enhance chaos in a reversible manner. Through our unique approach, we have succeeded in making chaos both chaotic and reversible, without the need for additional data. Demonstrating our system in a live demonstration would provide a comprehensive understanding of its capabilities.

The byte system undergoes a transformative process, becoming a separate object associated with two MAT tables. Each table contains associations between 128 bytes and a seven-bit transformation for decoding. This structure provides sufficient power for encryption. Switching tables is accomplished by absorbing a non-data bit as a valid bit within a byte, resulting in the complete destruction of the structure in any possible combination.

Based on the working principle of our algorithm, we have developed a white noise compression technique. The byte movement within the block of 256 bytes creates a structure resembling white noise patterns. Utilizing this property, we can compress data using white noise patterns, enhancing the efficiency and effectiveness of our encryption system.

In summary, our encryption system offers a unique approach to data security through chaotic byte movement and white noise compression. By modifying compression principles and harnessing the power of MAT tables and valid bit absorption, we have created a robust and reversible encryption environment. Demonstrations of our system would further emphasize its capabilities and highlight its practicality in real-world applications.

The transition from 8(to)7 is revolutionizing the landscape of data as we currently perceive it. We understand that there will be questions on our algorithms and codes, particularly on the methods we employed to achieve this compressor. Our existing quantum-resistant encryption (you can already find the open-source code here) coupled with our compressor forms a formidable combination. Should you have questions or wish to engage in discussion or even collaborate about this, feel free to contact us.on our GitHub page

What is the focus of 8-to-7 Technology?

Answer:

One of 8-to-7 Technology is in the compression of white noise.

Question 2:

How much lossless compression of white noise can the patented technology achieve?Answer:

The technology can achieve up to 12.5% lossless compression of white noise.

Question 3:

Why is the compression of white noise considered an unfamiliar concept to many

organizations?

Answer: White noise compression is not commonly explored by many organizations,

making it unfamiliar to them.

Question 4:

What components enable the successful compression of white noise.

Answer: The embedded compressor and unique methodology are key components

for achieving successful white noise compression.

.Question 5:

How canindividuals verify the claims about tthe white noise compression

technology?.

Answer:

Interested parties are invited to test tthe scientific methodology in neutral

environments and under various conditions.

Question 6:

What is the invitation extended to those with a scientific interest in the compression

technology

Answer:

They are encouraged to observe, examine, inspect, and analyze the technology.

.Question 7:

Why is the term”losslesscompression”significant in the context of whitenoise

compression?.

Answer: Lossless compression ensures that the compressed white noise retains its

original quality without any data loss.

Question 8:

How does the technology distinguish itself ffrom others in the field of white

noise compression?

Answer: The technology’s uniqueness lies in its patented compression

technology and distinctive approach.

Question 9:

What assurance is provided to those who want to evaluate the scientific methodology?

Answer: The text assures individuals that they can evaluate the methodology

in any neutral environment and under any conditions.

Question 10:

How can individuals express their interest in studying and analyzing the

compression technology?

Answer:

Interested individuals are encouraged to contact 8-to-7 Technology for

further information and arrangements.

Question 11:

What specific algorithmic techniques does 8-to-7 Technology employ in its

white noise compression?

Answer: 8-to-7 Technology utilizes advanced algorithmic techniques,

including a patented compression algorithm, to achieve efficient

white noise compression

.Question 12:

Could you elaborate on the unique methodology mentioned in achieving lossless

compression of white noise?

Answer: The unique methodology involves a combination of signal processing

techniques, predictive coding, and entropy encoding to minimize

redundancy and achieve optimal compression ratios.

Question 13:

What parameters or metrics define the success of the embedded

compressor in the compression process?

Answer: The embedded compressor is evaluated based on its ability to effectively

reduce data size while preserving the integrity of the white

noise signal, measured in terms of compression ratio and signal-to-noise ratio.

Question 14:

In what ways does the patented compression technology address challenges specific to

white noise compression?

Answer: The patented technology incorporates adaptive compression

mechanisms that dynamically respond to the characteristics of white noise,

mitigating challenges such as variable frequency content and amplitude fluctuations.

Question 15:

How does 8-to-7 Technology ensure the reproducibility of white noise after compression, and what role does lossless compression play in this context?

Answer: Lossless compression ensures that the original white

noise signal can be precisely reconstructed from the compressed data,

guaranteeing faithful reproduction without any degradation.

Question 16:

Can you provide insights into the computational complexity of the compression

algorithm and its impact on real-time applications?

Answer: The compression algorithm is designed to be computationally

efficient, with low complexity, making it suitable for real-time

applications where processing speed is crucial.

Question 17:

How does the technology adapt to different environmental conditions, and what

mechanisms are in place for robust performance?

Answer: Adaptive algorithms within the technology allow it to dynamically adjust to

varying environmental conditions, ensuring robust performance under diverse

scenarios.

Question 18:

Regarding the white noise compression’s impact on hardware resources, how

resource-intensive is the compression process?

Answer: The compression process is optimized to minimize resource

consumption, considering factors such as memory usage and processor

load to ensure efficient utilization of hardware resources.

Question 19:

Can you elaborate on the role of entropy encoding in achieving

compression efficiency, and how is it integrated into the overall compression framework?

Answer: Entropy encoding is employed to further reduce data size by assigning

shorter codes to more frequent symbols. It is integrated into the compression

framework to enhance overall efficiency and achieve higher compression ratios.

Question 20:

For those interested in delving deeper into the technical aspects,

what documentation or resources does 8- to-7 Technology provide for

understanding the inner workings of the compression technology?

Answer: 8-to-7

Technology offers technical documentation, white papers, and access to

source code (where applicable) for individuals with a

technical interest in understanding the intricacies of the

compression technology.

What is 8(to)7 Encryption?

8(to)7 Encryption is a type of encryption technology developed by NaveoI. It is a symmetric encryption algorithm that uses an 8-byte key to encrypt data. The 8-byte key is then reduced to a 7-byte key by using a hash function. This makes it more secure than other encryption algorithms, as it requires a longer key to decrypt the data.

What companies can use 8(to)7

8(to)7 Encryption can be used by any company or organization that wants to secure its data. It is especially useful for large organizations that have a lot of sensitive data to protect.

Why is 8(to)7 Encryption considered the best

8(to)7 Encryption is considered the best because it is more secure than other encryption algorithms. It is also simpler and easier to implement than other encryption algorithms

.

Why is 8(to)7 Encryption the best solution for large organizations

8(to)7 Encryption is the best solution for large organizations because it is more secure than other encryption algorithms. It also requires less computing power and is easier to implement. Furthermore, it is more secure than other encryption algorithms, as it requires a longer key to decrypt the data.

What makes 8(to)7 Encryption ideal to use for cybersecurity?

8(to)7 Encryption is an ideal solution for cybersecurity because it is an end-to-end encryption system that is designed to protect data from being accessed or stolen. It uses advanced encryption algorithms to ensure that data is secure and can only be accessed by authorized users. 8(to)7 also uses a unique key to encrypt and decrypt data, ensuring that the data is only accessible to those who have the key

.

Why is 8(to)7 also ideal for startups?

8(to)7 is also ideal for startups because it is easy to set up and use, and is cost-effective. It is also scalable, so as a company grows, its encryption system can grow with it. 8(to)7 is also designed to be secure, so startups can rest assured that their data is safe from cyber-attacks.

Why 8(to)7 is the Encryption of the future every company needs to use?

Finally, 8(to)7 is the encryption of the future because it is designed to be secure, cost-effective, and easy to use. As cyber-attacks become more sophisticated, companies need to ensure that their data is secure and 8(to)7 provides an encryption solution that meets these needs. As companies become more aware of the need for encryption, 8(to)7 will become an increasingly popular encryption solution.

Why is 8(to)7 offering their Encryption solution for free to Non-profit organizations?

8(to)7 is offering their Encryptions solution for free to Non-profit organizations because they believe in helping organizations with limited resources to protect their data and keep it secure. They understand that non-profits may have limited budgets and providing free encryption solutions allows them to remain secure without having to invest in expensive software. 8(to)7 also believes in supporting the communities in which they operate and feel that providing free encryption solutions to non-profits is a great way to do this.

Why do companies trust their security challenges with 8(to)7?

Companies trust their security challenges with 8(to)7 due to their commitment to providing comprehensive, reliable, and cost-effective cybersecurity solutions. 8(to)7 offers a comprehensive suite of security services, including advanced threat protection, compliance and risk management, and incident response. 8(to)7’s team of experienced security professionals is constantly monitoring and responding to emerging security threats. In addition, 8(to)7’s a cloud-based platform provides customers with the flexibility to deploy their security solutions quickly and cost-effectively. Finally, 8(to)7 provides customers with 24/7/365 support, ensuring that any security issues are addressed quickly and efficiently.

What is 8(to)7molucair AI-based encryption?

8(to)7molucair AI-based encryption is an advanced encryption technology that uses artificial intelligence (AI) algorithms to encrypt data. It is designed to provide superior security and privacy protection compared to traditional encryption methods. The encryption algorithms used by 8(to)7molucair AI-based encryption are constantly updated to stay ahead of new threats and vulnerabilities.

Why is 8(to)7 molecular AI-based encryption considered to be unhackable?

8(to)7 molecular AI-based encryption is considered to be unhackable because it uses a combination of quantum cryptography, artificial intelligence, and molecular computing to generate a unique encryption key for each file or message. This encryption key is extremely difficult to crack as it is constantly changing and is unique to each file or message. Additionally, the encryption algorithm is constantly evolving and adapting to changing security threats, making it even more difficult to crack.

Why is 8(to)7 molecular AI-based encryption considered one of the leaders in cyber security?

8(to)7 molecular AI-based encryption is considered one of the leaders in cyber security because it offers a unique approach to data encryption. It uses artificial intelligence to identify and analyze patterns in data, allowing it to create strong encryption keys that are difficult to break. Additionally, 8(to)7’s encryption technology is designed to be highly secure and resistant to quantum computing attacks. This makes it an ideal choice for organizations that need to ensure the security of their sensitive data.

How to try or buy 8(to)7 Encryption solutions?

8(to)7 Encryption solutions can be purchased and tried through their website. Customers can purchase the product directly from their website and receive the software via email. They also offer a free trial version of their software that provides full access to their features and functions. Customers can also contact 8(to)7 Encryption directly to discuss buying and trying their solutions.

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Why is 8(to)7 encryption faster than Homomorphic encryption?

8(to)7 encryption is faster than Homomorphic encryption because it is a symmetric encryption algorithm, which means that the same key is used to encrypt and decrypt the data. This makes the encryption and decryption process much faster than with Homomorphic encryption, which uses an asymmetric encryption algorithm and requires two keys (a public and a private key) for the encryption and decryption process.

Why is 8(to)7 encryption faster than AES 256 encryption?

8(to)7 encryption is faster than AES 256 encryption because it uses fewer rounds of encryption. 8(to)7 uses 8 rounds of encryption, while AES 256 uses 14 rounds of encryption. Since 8(to)7 requires fewer rounds of encryption, it is able to process data more quickly than AES 256.

Why is 8(to)7 encryption faster than Blowfish encryption?

8(to)7 encryption is faster than Blowfish encryption because it uses less rounds of encryption, which makes it faster to process. 8(to)7 also uses smaller key sizes, which also helps to speed up the encryption process. In addition, 8(to)7 is designed to be resistant to side-channel attacks, which can further improve its speed.

8(to)7 has a format with that we can compress using this principle. At the moment it is 65% of the original, and a well-known archiver compresses it only by 10%

we can compress any data arrays in which 256 bytes are evenly distributed over a block of 256 bytes. the rearrangement of these bytes in each block has no absolute meaning and does not carry any additional metadata. our encryption system is based on data compression since we were able to separate the entropy from the main file and then compress this array by 12.5%. At this stage of development, we are not yet able to work with all existing code such as MP3 video formats and graphic formats, but We have many solutions that allow you to do this. so far we have focused on a coding system based on the molecular genome principle that creates an eco-array. it’s difficult to explain in a nutshell, it needs to be shown. but at this stage we can clearly state that we can simplify files and then complicate them without additional metadata. On our website this is a general idea of data compression.

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