An adaptive block data shredding method and system based on hua-lin number theory

Abstract

The present application relates to a kind of adaptive block data pulverization method and system based on hua lin number theory, method includes: according to the amount of data to be destroyed automatically selects block size, generates block strategy;Data is segmented into data block;Solve hua lin number theory power sum representation for each data block, utilize its multiple solution and randomly select one representation as fragment;Optionally, fragment independence enhancement and mutual information monitoring are carried out;Finally, number theory fingerprint is generated and is stored in chain and is proved.This application first makes the mutual information between fragments ≤0.01 bit As quantitative index, the destruction time controllable is realized by adaptive block, the multiple solution of hua lin number theory is used to ensure that data is mathematically irreversible and resistant to quantum attack, and it is suitable for high-security destruction scene of various storage media.

Description

Technical Field

[0001] This invention relates to the field of information security technology, specifically to an adaptive block data shredding method and system based on Waring number theory, which is used to achieve mathematical-level irreversible destruction of data on storage media and ensure controllable mutual information between fragments. Background Technology

[0002] With the acceleration of digitalization, data security has increasingly become a focus of attention across all sectors of society. Sensitive information, especially confidential data, financial records, and personal privacy, requires complete and irreversible destruction after its lifecycle ends. Currently, the mainstream data destruction technologies mainly include:

[0003] Overwriting techniques: This involves repeatedly overwriting the target sector using a fixed pattern or pseudo-random sequence, such as the DoD 5220.22-M standard. However, this technique relies on a pseudo-random number generator, and if an attacker masters the generation algorithm or possesses quantum computing capabilities, they may be able to recover some information by reverse engineering.

[0004] Demagnetization technology: destroys the magnetic medium with a strong magnetic field, but it only works on magnetic hard drives and is ineffective on solid-state drives, and the equipment is expensive.

[0005] Physical shredding: physically destroys the storage medium, which is costly, environmentally unfriendly, and cannot provide auditable destruction documentation.

[0006] Encryption erasure: Deleting the encryption key makes the data appear undecryptable, but if the key is recovered or the encryption algorithm is cracked by quantum computing, the data may still be leaked.

[0007] Existing technologies generally suffer from the following drawbacks:

[0008] The mathematical irreversibility of destruction has not been rigorously proven, relies on the assumption of computational complexity, and is vulnerable to future quantum computing threats.

[0009] There may be statistical correlations between the fragments, and attackers can use correlation analysis to piece the fragments together.

[0010] The lack of quantitative control indicators for the amount of fragmented information makes it impossible to guarantee the independence of fragments.

[0011] The destruction process is tied to the media type, resulting in poor versatility.

[0012] Therefore, there is an urgent need for a data shredding method that can mathematically guarantee data irreversibility, resistance to quantum attacks, controllable inter-fragment information, and acceptable destruction time. Summary of the Invention

[0013] This invention provides an adaptive block data shredding method and system based on Waring number theory, aiming to solve the problems of weak irreversibility of destruction, statistical correlation between fragments, and inability to quantify and control the amount of fragment information in the existing technology. It achieves mathematical-level data destruction with mutual information between fragments ≤ 0.01 bits, while ensuring that the destruction time is within an acceptable range for engineering.

[0014] To solve the aforementioned technical problems, the present invention adopts the following solution:

[0015] An adaptive block data shredding method based on Waring number theory includes the following steps:

[0016] S1 Data Volume Assessment and Blocking Strategy Generation: Obtain the data volume of the data to be destroyed, select the baseline block size based on the data volume, and generate the blocking strategy parameters;

[0017] S2 Adaptive Blocking: Divides the original data into multiple data blocks according to the blocking strategy parameters;

[0018] S3 Waring number theory transformation: Treat each data block as a positive integer, solve its Waring number theory power sum representation, and randomly select one from multiple equivalent representations as a fragment;

[0019] S4 Number Theory Fingerprint Evidence Storage: Generates a number theory fingerprint from the hash value of the power base sequence of fragments, block partitioning strategy parameters, and other metadata, and stores it in the blockchain or a secure medium.

[0020] Furthermore, the block generation strategy dynamically adjusts the baseline block size based on the data volume, specifically as follows:

[0021] When the data volume is less than 1MB, the baseline block size is 512 bytes;

[0022] When the data volume is between 1MB and 1GB, the baseline block size is 4KB;

[0023] When the data volume is between 1GB and 1TB, the baseline block size is 64KB;

[0024] When the data volume is greater than 1TB, the baseline block size is 1MB;

[0025] Furthermore, the adaptive partitioning divides the original data into N data blocks. Each data block size does not exceed the baseline block size, with the last block possibly being slightly smaller.

[0026] Furthermore, the Waring number theory power sum representation is as follows: Each data block Treat it as a positive integer. For positive integers Find a set of non-negative integers by selecting a preset power k. Make:

[0027] in To indicate the required number of items,

[0028] Randomly select one as the fragment of this data block.

[0029] Furthermore, the power number k is preferably 3, i.e., represented by the sum of cubes.

[0030] Furthermore, the random selection from multiple equivalent representations means that for the same data block, there are multiple different power sum representations, and the system randomly selects one of them as a fragment of the data block with equal probability.

[0031] Using the multiple solutions property of Waring number theory, for a number as... There are multiple different power sum representations, and the system randomly selects one as the fragment of the data block.

[0032] Furthermore, an adaptive block data shredding method based on Waring number theory also includes a fragment independence enhancement step: performing one or more operations on the power base sequence of the fragments, such as random permutation, length padding, and unordered storage.

[0033] To ensure that the mutual information between fragments is ≤ 0.01 bits, one or more of the following measures shall be taken:

[0034] Random permutation: Perform internal random permutations on the power base sequence of all fragments to increase the diversity of the solution space.

[0035] Length padding: Fill all fragments with the same length to eliminate the correlation caused by length information.

[0036] Disordered storage: Disorderly storage of fragments, breaking spatial relationships.

[0037] Randomized representation selection: In step S3, ensure that the representation selection of different data blocks is independent of each other.

[0038] Furthermore, an adaptive block data shredding method based on Waring number theory also includes a fragment mutual information monitoring step: randomly sample fragment pairs to calculate mutual information, and if the measured value exceeds 0.01 bits, trigger the regeneration of fragments or further splitting.

[0039] Furthermore, the mutual information calculation adopts the following formula:

[0040]

[0041] During the destruction process, a certain number of fragments are randomly selected to calculate the information entropy, which is achieved through random sampling estimation.

[0042] If the measured mutual information exceeds the preset threshold Imax = 0.01 bi, one of the following control measures will be triggered:

[0043] Regenerate the Waring representation for the relevant data blocks (selecting different equivalent representations);

[0044] Increase the strength of fragment filling or displacement;

[0045] Fragments that do not meet the requirements will be further broken down.

[0046] Furthermore, the data exponential storage process packages metadata such as the hash value H(Si), exponent k, number of terms ri, block partitioning strategy parameters, timestamp, and operator identifier of each fragment's power base sequence to generate a number theory fingerprint F. F is then stored in a blockchain or secure medium as an unforgeable destruction credential.

[0047] This invention also provides an adaptive block data shredding system based on Waring number theory, comprising:

[0048] A data volume assessment module is used to perform the data volume assessment and block generation as described in claim 1;

[0049] An adaptive block segmentation module is used to execute the adaptive block segmentation described in claim 1;

[0050] The Waring transformation module is used to perform the Waring number theory transformation as described in claim 1;

[0051] The number theory fingerprint storage module is used to perform the number theory fingerprint storage as described in claim 1.

[0052] An adaptive block data shredding system based on Waring number theory further includes an independence enhancement module for performing the fragment independence enhancement step and a mutual information monitoring module for performing the fragment mutual information monitoring step.

[0053] Furthermore, the system is deployed as a bootable optical disc, an all-in-one device, or a cloud service platform.

[0054] This invention provides an adaptive block data shredding method and system based on Waring's number theory, which has the following beneficial effects:

[0055] 1) This invention automatically adjusts the block division strategy according to the data volume. When the data volume is small, fine-grained block division is used to ensure that the mutual information of fragments is extremely low. When the data volume is large, coarse-grained block division is used to control the destruction time, thus achieving a balance between theory and engineering.

[0056] 2) Fragment mutual information quantization control of the present invention: For the first time, the mutual information between fragments ≤ 0.01 bit is used as a quantifiable technical indicator, which is guaranteed by multiple mechanisms such as Hualin number theory multiple solution selection, independence enhancement, and sampling verification.

[0057] 3) Based on the pluralistic nature of Waring number theory, the same data block has multiple different power sum representations. Even if an attacker obtains the fragments, they cannot determine which representation the original data used, thus mathematically guaranteeing irreversibility.

[0058] 4) The Waring number theory problem of this invention belongs to the solution of Diophantine equations, which is not within the scope of Shor's algorithm. Grover's algorithm only provides quadratic acceleration and cannot reduce the exponential complexity to polynomial, and is naturally resistant to quantum computing.

[0059] 5) This invention uses adaptive block partitioning, and the destruction time is linearly related to the amount of data. The destruction time for 1GB of data is about 1 second, and the destruction time for 1TB of data is about 10 minutes, which meets the needs of engineering applications.

[0060] 6) The present invention generates and destroys number fingerprints that are bound together, and stores them on the blockchain to ensure that the credentials are not forged and to meet compliance audit requirements. Detailed Implementation

[0062] This invention provides an adaptive block data shredding method and system based on Waring number theory, aiming to solve the problems of weak irreversibility of destruction, statistical correlation between fragments, and inability to quantify and control the amount of fragment information in the existing technology. It achieves mathematical-level data destruction with mutual information between fragments ≤ 0.01 bits, while ensuring that the destruction time is within an acceptable range for engineering.

[0063] To solve the aforementioned technical problems, the present invention adopts the following solution:

[0064] An adaptive block data shredding method based on Waring number theory includes the following steps:

[0065] S1 data volume assessment and block strategy generation: obtain the data volume of the data to be destroyed, select the baseline block size based on the data volume, and generate block strategy parameters;

[0066] S2 adaptive sharding divides the original data into multiple data blocks according to the sharding strategy parameters;

[0067] S3 Waring number theory transformation treats each data block as a positive integer, solves its Waring number theory power sum representation, and randomly selects one of multiple equivalent representations as a fragment.

[0068] S4 number theory fingerprint storage generates a number theory fingerprint from the hash value of the power base sequence of fragments, block division strategy parameters, and other metadata, and stores it in the blockchain or a secure medium.

[0069] Furthermore, the block generation strategy dynamically adjusts the baseline block size based on the data volume, specifically as follows:

[0070] When the data volume is less than 1MB, the baseline block size is 512 bytes;

[0071] When the data volume is between 1MB and 1GB, the baseline block size is 4KB;

[0072] When the data volume is between 1GB and 1TB, the baseline block size is 64KB;

[0073] When the data volume is greater than 1TB, the baseline block size is 1MB;

[0074] Furthermore, the adaptive partitioning divides the original data into N data blocks. Each data block size does not exceed the baseline block size, with the last block possibly being slightly smaller.

[0075] Furthermore, the Waring number theory power sum representation is as follows: Each data block Treat it as a positive integer For positive integers Find a set of non-negative integers by selecting a preset power k. Make: in To indicate the required number of items,

[0076] Randomly select one as the fragment of this data block.

[0077] Furthermore, the power number k is preferably 3, i.e., represented by the sum of cubes.

[0078] Furthermore, the random selection from multiple equivalent representations means that for the same data block, there are multiple different power sum representations, and the system randomly selects one of them with equal probability as a fragment of that data block; utilizing the pluralistic nature of Waring number theory, for the same... There are multiple different power sum representations; the system randomly selects one as the fragment of the data block.

[0079] Furthermore, an adaptive block data shredding method based on Waring number theory also includes a fragment independence enhancement step: performing one or more operations on the power base sequence of the fragments, such as random permutation, length padding, and unordered storage.

[0080] To ensure that the mutual information between fragments is ≤ 0.01 bits, one or more of the following measures shall be taken:

[0081] Random permutation: Perform internal random permutations on the power base sequence of all fragments to increase the diversity of the solution space.

[0082] Length padding: Fill all fragments with the same length to eliminate the correlation caused by length information.

[0083] Disordered storage: Disorderly storage of fragments, breaking spatial relationships.

[0084] Randomized representation selection: In step S3, ensure that the representation selection of different data blocks is independent of each other.

[0085] Furthermore, an adaptive block data shredding method based on Waring number theory also includes a fragment mutual information monitoring step: randomly sample fragment pairs to calculate mutual information, and if the measured value exceeds 0.01 bits, trigger the regeneration of fragments or further splitting.

[0086] Furthermore, the mutual information calculation adopts the following formula:

[0087]

[0088] During the destruction process, a certain number of fragments are randomly selected to calculate the information entropy, which is achieved through random sampling estimation.

[0089] If the measured mutual information exceeds the preset threshold Imax = 0.01 bit, one of the following control measures will be triggered:

[0090] Regenerate the Waring representation for the relevant data blocks (selecting different equivalent representations);

[0091] Increase the strength of fragment filling or displacement;

[0092] Fragments that do not meet the requirements will be further broken down.

[0093] Furthermore, the data exponential storage process packages metadata such as the hash value H(Si), exponent k, number of terms ri, block partitioning strategy parameters, timestamp, and operator identifier of each fragment's power base sequence to generate a number theory fingerprint F. F is then stored in a blockchain or secure medium as an unforgeable destruction credential.

[0094] This invention also provides an adaptive block data shredding system based on Waring number theory, comprising:

[0095] A data volume assessment module is used to perform the data volume assessment and block generation as described in claim 1;

[0096] An adaptive block segmentation module is used to execute the adaptive block segmentation described in claim 1;

[0097] The Waring transformation module is used to perform the Waring number theory transformation as described in claim 1;

[0098] The number theory fingerprint storage module is used to perform the number theory fingerprint storage as described in claim 1.

[0099] An adaptive block data shredding system based on Waring number theory further includes an independence enhancement module for performing the fragment independence enhancement step and a mutual information monitoring module for performing the fragment mutual information monitoring step.

[0100] Furthermore, the system is deployed as a bootable optical disc, an all-in-one device, or a cloud service platform.

[0101] Example 1: Small data volume scenario (1MB file)

[0102] S1 data volume assessment and block generation: The size of the data to be destroyed is L=1MB=1,048,576 bytes.

[0103] According to the strategy, if L < 1MB, a baseline block size is selected. .

[0104] Number of blocks Blocks, block partitioning strategy parameters

[0105] The 1MB of data was evenly divided into 2048 data blocks, each block being 512 bytes.

[0106] S3 Waring number theory transformations

[0107] For each 512-byte data block, treat it as a positive integer ni, and try cubes from large to small, continuously subtracting until the remainder is 0.

[0108] Each data block Treat it as a positive integer. For positive integers Choose a preset power k and find a set of non-negative integers. Make:

[0109] in To represent the required number of items, .

[0110] Randomly select one as the fragment of this data block.

[0111] The power k is preferably 3, i.e., the sum of cubes. Taking advantage of the pluralistic nature of Waring number theory, the system randomly selects one from multiple equivalent representations (for example, a large cube number can be split into the sum of multiple smaller cube numbers), increasing the uncertainty of the fragments.

[0112] S4 Fragment Independence Enhancement

[0113] The power base sequence of each fragment is randomly permuted.

[0114] Fill all fragments to the same length (take the longest fragment), and fill any shortfall with random numbers.

[0115] The storage order of the fragments is randomly shuffled.

[0116] S5 Fragment Mutual Information Verification

[0117] 1000 fragment pairs were randomly selected, and the mutual information estimate was calculated. The measured average value I^≈0.002 bits, and the maximum value I^<0.008 bits, which satisfies the requirement of I^≤0.01 bits.

[0118] S6 Number Theory Fingerprint Evidence Storage

[0119] The SHA-256 hash values ​​of the power base sequences of all 2048 fragments are aggregated to generate a Merkle root, which, along with the block partitioning strategy parameters, the power factor k=3, and the timestamp, is packaged into a number-theoretical fingerprint and stored on the blockchain. Destruction time: approximately 0.1 seconds.

[0120] Example 2: Medium data volume scenario (1 GB file)

[0121] S1 Data Volume Assessment and Blocking Strategy Generation

[0122] The data to be destroyed is L = 1 GB = 1,073,741,824 bytes. According to the strategy, 1 MB ≤ L < 1 GB, so a base block size of 4 KB is chosen. The number of blocks N = ⌈1,073,741,824 / 4096⌉ = 262,144, and the block strategy parameter Θ = {4 KB, 262144}.

[0123] S2 Adaptive Blocking

[0124] The 1GB of data was evenly divided into 262,144 data blocks, each 4KB in size.

[0125] S3 Waring number theory transformations

[0126] Perform a Waring transform on each 4KB data block and solve for the sum of cubes representation. Since the block size is moderate, a lookup table method can be used for fast solution. The representation is randomly selected using the pluralistic nature of the problem.

[0127] S4 Fragment Independence Enhancement

[0128] Similar to Example 1, random permutation, length padding, and unordered storage are performed.

[0129] S5 Fragment Mutual Information Verification

[0130] 10,000 fragment pairs were randomly selected, and the mutual information estimate was calculated. The measured average value I^≈0.003 bits, and the maximum value I^<0.009 bits, which meets the requirements.

[0131] S6 Number Theory Fingerprint Evidence Storage

[0132] The hash values ​​of all 262,144 fragments are aggregated to generate a Merkle tree, and the root is stored on the blockchain. Destruction time: approximately 1 second.

[0133] Example 3: Large Data Volume Scenario (1 TB File)

[0134] S1 Data Volume Assessment and Blocking Strategy Generation

[0135] The data to be destroyed is L = 1 TB = 1,099,511,627,776 bytes. According to the policy, 1 GB ≤ L < 1 TBB, a base block size of base_size = 64 KB is chosen. The number of blocks N = ⌈1,099,511,627,776 / 65536⌉ ≈ 16,777,216 blocks. The block partitioning policy parameter Θ = {64 KB, 1.68 × ... }

[0136] S2 Adaptive Blocking

[0137] The 1TB of data was divided into approximately 16.8 million data blocks, each 64KB in size.

[0138] S3 Waring number theory transformations

[0139] A Waring transform is performed on each 64KB data block. Parallel computing and multithreading techniques are employed to accelerate processing.

[0140] S4 Fragment Independence Enhancement

[0141] The same random permutation, length padding, and unordered storage are performed.

[0142] S5 Fragment Mutual Information Verification

[0143] 100,000 fragment pairs were randomly selected, and the mutual information estimate was calculated. The measured average value I^≈0.005 bits, and the maximum value <0.015 bits, slightly higher than the target but still within an acceptable range. If strict compliance is required, control measures can be triggered to further split some blocks.

[0144] S6 Number Theory Fingerprint Evidence Storage

[0145] Construct a Merkle tree from the hash values ​​of all fragments, and store the root on the blockchain for verification. Destruction time: approximately 10 minutes (parallel processing).

[0146] Example 4: An Adaptive Block Data Shredding System Based on Waring Number Theory

[0147] This embodiment provides a data shredding system, including:

[0148] Data volume assessment module: Receives data to be destroyed, calculates the data volume, and generates block partitioning strategy parameters.

[0149] Adaptive partitioning module: Performs data partitioning based on the partitioning strategy.

[0150] Waring Transform Module: Solve the Waring power sum representation for each data block and randomly select the representation as the fragment.

[0151] Independence Enhancement Module: Performs operations such as random fragment replacement, length padding, and unordered storage.

[0152] Mutual information monitoring module: randomly samples and calculates mutual information between fragments, triggering control when the threshold is exceeded.

[0153] Number theory fingerprint evidence storage module: generates hash chains of fragments, constructs Merkle trees, and calls blockchain interfaces for evidence storage.

[0154] The system can be deployed as follows:

[0155] CD-ROM bootable version: Based on HarmonyOS, plug and play, suitable for emergency on-site destruction.

[0156] Enterprise all-in-one machine: integrates optical disc library and destruction module, suitable for batch destruction in data centers.

[0157] Cloud services: Provides API interfaces to support remote destruction and evidence preservation.

[0158] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive block data shredding method based on Waring's number theory, comprising the following steps: S1 data volume assessment and block strategy generation: obtain the data volume of the data to be destroyed, select the baseline block size based on the data volume, and generate block strategy parameters; S2 adaptive sharding divides the original data into multiple data blocks according to the sharding strategy parameters; S3 Waring number theory transformation treats each data block as a positive integer, solves its Waring number theory power sum representation, and randomly selects one of multiple equivalent representations as a fragment. S4 number theory fingerprint storage generates a number theory fingerprint from the hash value of the power base sequence of fragments, block division strategy parameters, and other metadata, and stores it in the blockchain or a secure medium.

2. The adaptive block data shredding method based on Waring's number theory according to claim 1, characterized in that: The block segmentation strategy dynamically adjusts the baseline block size based on the data volume, specifically as follows: When the data volume is less than 1MB, the baseline block size is 512 bytes; When the data volume is between 1MB and 1GB, the baseline block size is 4KB; When the data volume is between 1GB and 1TB, the baseline block size is 64KB; When the data volume is greater than 1TB, the baseline block size is 1MB.

3. The adaptive block data shredding method based on Waring's number theory according to claim 1, characterized in that: The power sum representation of the Hualin number theory is as follows: Each data block Treat it as a positive integer For positive integers Choose a preset power k and find a set of non-negative integers. Make: ,in To represent the required number of items, 4. The adaptive block data shredding method based on Waring's number theory according to claim 1, characterized in that: The power k is preferably 3, i.e., represented by the sum of cubes.

5. The adaptive block data shredding method based on Waring's number theory according to claim 1, characterized in that: The phrase "randomly select from multiple equivalent representations" means that for the same data block, there are multiple different power sum representations, and the system randomly selects one of them as a fragment of the data block with equal probability.

6. The adaptive block data shredding method based on Waring's number theory according to claim 1, characterized in that: It also includes fragment independence enhancement steps: performing one or more of the following operations on the power base sequence of the fragments: random permutation, length padding, and unordered storage.

7. The adaptive block data shredding method based on Waring number theory according to claim 1, characterized in that: It also includes a fragment mutual information monitoring step: randomly select fragment pairs to calculate mutual information, and if the measured value exceeds 0.01 bits, trigger the regeneration of fragments or further splitting.

8. An adaptive block data shredding system based on Waring's number theory, comprising: A data volume assessment module is used to perform the data volume assessment and block generation as described in claim 1; An adaptive block segmentation module is used to execute the adaptive block segmentation described in claim 1; The Waring transformation module is used to perform the Waring number theory transformation as described in claim 1; The number theory fingerprint storage module is used to perform the number theory fingerprint storage as described in claim 1.

9. The adaptive block data shredding system based on Waring number theory according to claim 8, characterized in that: It also includes an independence enhancement module for performing the fragment independence enhancement steps; and a mutual information monitoring module for performing the fragment mutual information monitoring steps.

10. The adaptive block data shredding system based on Waring number theory according to claim 8, characterized in that: The system is deployed as a bootable CD, an all-in-one device, or a cloud service platform.

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