A data processing system and method

Abstract

The application belongs to the technical field of data processing, and particularly relates to a data processing system and method, which comprises the following steps: S1, segmenting important data segments according to the importance of data based on keywords on a client side; S2, performing equal-length segment encryption on the important data segments based on an asymmetric encryption algorithm, performing symmetric encryption on the private key of the equal-length data segments, and combining a quantum key and a one-way hash function to obtain a symmetric encryption key; S3, randomly transmitting the encrypted data segments to a cloud platform server, performing twice hash calculation on the cloud platform server, returning the hash values to the client side for comparison, and determining whether to send an actual data segment combination sequence to the cloud platform server according to the comparison results. The application solves the problem of data encryption security in the process of transmitting data from the client side to the cloud platform server in actual application.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to a data processing system and method. BACKGROUND

[0002] With the rapid development of Internet technology, more and more data needs to be transmitted between a client and a cloud platform server. However, the data is extremely vulnerable to interception and theft in the transmission process, seriously threatening the data security of users. In order to solve this problem, the current industry begins to use data encryption technology to ensure the confidentiality and integrity of the data in the transmission process by encrypting the client data.

[0003] Application No. CN105635144B discloses a data processing method and system based on a cloud platform server. The method first encrypts the client data simply, then transmits the encrypted data to the cloud platform server, and the cloud platform server encrypts the encrypted data again and stores it. The application improves the security of the stored data in the cloud platform server, so that the cloud platform server will not cause user data leakage when being attacked and invaded. In the above application, if the client data is transmitted to the cloud platform server and is attacked and invaded by the outside world, the data is extremely vulnerable to leakage because the real data of the client is closest to this stage. Once the data is leaked, it cannot be recovered, so it is urgent for the technical personnel in the field to solve the above technical problems. SUMMARY

[0004] In order to solve the above technical problems in the prior art, the data processing system has poor security, and if it is attacked and invaded by the outside world, data leakage is easy to occur.

[0005] In order to achieve the above purpose, the main technical scheme adopted by the application includes:

[0006] A data processing method, comprising the following steps:

[0007] S1, segmenting important data segments according to the importance of the data according to keywords on the client side;

[0008] S2, performing equal-length segment encryption on the important data segments according to an asymmetric encryption algorithm, performing symmetric encryption on the private key of the equal-length data segments, and combining a quantum key and a one-way hash function to obtain a symmetric encryption key;

[0009] S3, randomly transmitting the encrypted data segments to the cloud platform server, performing twice hash calculation on the cloud platform server, returning the hash values to the client side for comparison, and determining whether to send the actual data segment combination sequence to the cloud platform server according to the comparison results.

[0010] Further, the specific way of dividing the important data segment from the data in step S1 is:

[0011] Creating a keyword list, which contains several keywords representing the importance of the content;

[0012] Reading the entire set of data line by line, if the part containing the keyword is found, mark the data segment where it is located;

[0013] For the marked part containing the keyword, expand the range forward or backward to determine the complete boundary of the important segment;

[0014] Remove the important segment part that has been marked and determined, and the remaining data content is uniformly classified as a common segment.

[0015] Further, the method of creating a keyword list is:

[0016] S21: Quickly browse the entire set of data to find words that suggest important content. For data with clear format specifications, pay special attention to the formatted part and extract keywords from it;

[0017] S22: Collect action or descriptive words related to important content. For data in a specific field, extract keywords in combination with domain knowledge;

[0018] S23: For the extracted keywords, find their synonyms and near synonyms;

[0019] S24: Exclude words unrelated to the data theme.

[0020] Further, the method of confirming the complete boundary of the important segment is:

[0021] From the important point to the initial point number associated with the initial association of the important point, and from the important point to the end point number where the important point is described, the initial point number associated with the initial association of the important point to the end point number where the important point is described as the complete boundary of the important segment.

[0022] Further, in step S2, the encryption algorithm for the common data segment includes RSA algorithm, ECC algorithm, and the encryption algorithm for the important data segment includes lattice-based cryptography algorithm, ElGamal algorithm and Paillier algorithm.

[0023] Further, the method of equal-length segmentation of the important data segment according to the selected asymmetric encryption algorithm is:

[0024] Determine the total length L of the important data segment, where L is the number of bytes;

[0025] According to the selected asymmetric encryption algorithm, the length of each sub-segment after equal-length division is determined as S;

[0026] According to the formula: N=L / S, the number of sub-segments after division is calculated;

[0027] Starting from the beginning of the important data segment, the determined sub-segment length S is divided.

[0028] Further, the specific steps of combining symmetric encryption, quantum key, and one-way hash function to encrypt the private key are:

[0029] The client establishes a quantum communication connection with a trusted quantum key distribution server;

[0030] Through the quantum key distribution protocol, a set of quantum keys KQ is generated between the client and the server;

[0031] The client uses a one-way hash function to process the quantum key KQ, and the obtained fixed-length hash value KS is used as the key for symmetric encryption;

[0032] The generated symmetric encryption key KS is used to encrypt the private key.

[0033] Further, in step S3, the specific method of the cloud platform server for twice hash calculation and returning to the client for judgment is:

[0034] S31: The client adds an index to the encrypted data segment, and the index is a random natural number and cannot be repeated;

[0035] S32: The client randomly transmits the encrypted data segment to the cloud platform server, records the actual combination sequence X of the encrypted data segment, and combines the index of the transmitted encrypted data segment to obtain sequence Y, and calculates the hash value of sequence Y as H;

[0036] S33: The cloud platform server receives the encrypted data segment and extracts the index sequence Y1, calculates the hash value as H1, and returns H1 to the client;

[0037] S34: If H1=H, the client adds a number a to the b position of sequence Y using Logistic chaotic mapping according to X, calculates the hash value as H2, and returns (a, b) to the cloud platform server;

[0038] S35: The cloud platform server adds (a, b) to sequence Y1, calculates the hash value as H3, and returns H3 to the client;

[0039] S36: If H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

[0040] Further, the method for adding numbers to the sequence Y according to X by using the Logistic chaotic mapping is:

[0041] The original chaotic sequence {x1, x2,..., xn} is obtained by using the Logistic chaotic mapping for multiple iterations;

[0042] The original chaotic sequence is normalized according to the formula (xn-min(xn)) / (max(xn)-min(xn)) to obtain the normalized sequence {y1, y2,..., yn};

[0043] The maximum value and the minimum value of the sequence X are found as p and q, and the chaotic sequence {z1, z2,..., zn} in the range [q, p] is obtained by using the formula zn=q+(p-q)*yn;

[0044] The chaotic sequence {z1, z2,..., zn} is taken out one by one, and the first prime number obtained is taken as the inserted number a, and the second prime number is taken as the inserted position b, wherein [] is the integer symbol.

[0045] The application further discloses a data processing system, which uses the data processing method as the operation logic of the data processing system;

[0046] The data processing system comprises a data segmentation unit, a data encryption unit, a private key encryption unit, a random transmission unit and a hash judgment unit.

[0047] The data segmentation unit mainly performs segmentation operation on the data to be transmitted according to the key, divides the data into ordinary data segments and important data segments, and transmits the ordinary data segments and the important data segments to the data encryption unit.

[0048] The data encryption unit performs asymmetric encryption on the ordinary data segments and the important data segments respectively, and continues to divide the important data segments for encryption according to the selected asymmetric encryption algorithm of the important data segments, obtains a plurality of private keys, and transmits the private keys to the private key encryption unit.

[0049] The private key encryption unit generates a group of keys by using quantum key distribution, substitutes the group of keys into a one-way hash function to obtain a hash value, and uses the hash value as a symmetric encryption key to encrypt the private key, and transmits the encrypted data segment to the random transmission unit.

[0050] The random transmission unit randomly labels the encrypted data segment index of the client, randomly transmits the encrypted data segment, obtains the sequence X and Y, and obtains the hash value H of Y, and transmits Y to the hash judgment unit.

[0051] The hash judging unit extracts the subscript of the encrypted segmented data received by the cloud platform server to obtain a sequence Y1, obtains the hash value of Y1 as H1, if H=H1, the client adds a number a to the b position of the sequence Y according to X using the Logistic chaotic mapping, calculates the hash value as H2, and returns (a, b) to the cloud platform server, the cloud platform server adds (a, b) to the sequence Y1, calculates the hash value as H3 at this time, and returns H3 to the client, if H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

[0052] The present application has at least the following advantages:

[0053] 1. The present application encrypts important data segments in equal length by using an asymmetric encryption algorithm, and protects the private key by combining a quantum key and a one-way hash function, ensuring the high security of the data in the transmission process from the client to the cloud platform server, and further preventing the possibility of data interception or tampering by randomizing the transmission order and using the hash value to verify the integrity of the data, improving the security of data transmission, and effectively resisting the risk of external attacks and data leakage;

[0054] 2. The present application identifies and encrypts important data segments according to keywords, rather than encrypting all data with equal strength, which not only ensures that sensitive information is fully protected, but also reduces unnecessary computational overhead, improving overall data processing efficiency. For different types of asymmetric encryption algorithms (such as RSA, ECC), and for important data segments, higher level encryption algorithms (such as lattice-based cryptography, ElGamal and Paillier) are used, which embodies the high flexibility and adaptability of the scheme;

[0055] 3. The present application uses a unique double hash verification process to confirm the consistency of the data segment sequence transmitted between the client and the cloud platform server, and generates additional verification parameters (a, b) using the Logistic chaotic mapping on the client side, and interacts with the cloud platform server to complete the final hash value matching. This process not only increases the difficulty of cracking, but also provides a reliable data integrity and authenticity verification means for both parties, thereby ensuring the accuracy of the data in the transmission process, effectively preventing potential data tampering risks, and improving the credibility of the system. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The present application is a schematic diagram;

[0057] Figure 2 The present application is a system block diagram. DETAILED DESCRIPTION

[0058] Clearly, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0059] As Figure 1 A data processing method, comprising:

[0060] S1, segmenting data according to the importance of keywords on the client side, and dividing out important data segments;

[0061] The specific way of dividing out important data segments from data is:

[0062] Creating a keyword list, the keyword list containing a plurality of keywords reflecting the importance of content;

[0063] Reading the entire set of data line by line, and paying attention to whether the keywords in the keyword list appear;

[0064] If the part containing the keywords is found, mark the data segment where it is located;

[0065] For the marked part containing the keywords, extend the range forward or backward to determine the complete boundary of the important segment;

[0066] Extending the range forward or backward to determine the complete boundary of the important segment should follow the principle of semantic integrity and logical coherence, and trace back to the initial point number associated with the initial relevant point of the important point, and extend to the end point number where the important point is fully described;

[0067] The initial point number is generally a semicolon, a period, and a colon, etc., used to indicate a new sentence or paragraph to be described;

[0068] The end point number is generally a period, used to indicate that the description of the important segment has ended;

[0069] For example, the sentence containing the keywords is "key indicators: market share reached 30%, which is due to our efforts in channel expansion and product optimization in the first half of the year", which can be traced back to the complete sentence: "Looking back on this year, our team has been hardworking: in the first half of the year, we focused on a series of work aimed at improving product competitiveness and market share. Up to the colon before "the first half of the year", "the first half of the year, we focused on a series of work aimed at improving product competitiveness and market share" is included in the important segment, and the part describing the specific measures of channel expansion and product optimization is also included;

[0070] In addition to the marked important section, the remaining data content is classified as ordinary section;

[0071] The method of creating a keyword list is as follows:

[0072] Quickly browse the entire set of data to find words that suggest the importance of the content. For data with clear format specifications, first focus on these formatted parts and extract keywords from them;

[0073] Words that intuitively suggest importance include: key, important, core, essential, significant, crucial, decisive, indispensable, main, fundamental, priority, prominent, substantial, decisive, and major, etc.

[0074] For data with clear format specifications, such as academic and scientific papers, these data usually have some subheadings, such as research focus, core conclusions, etc., which can indicate that the data here is more important than other places;

[0075] Find action or descriptive words related to important content. For data in specific fields, extract keywords in combination with domain knowledge;

[0076] Action words usually represent some behavior, process or activity, such as some news reports, where "growth", "reduction", "improvement" are action words, used to describe the core action of news events;

[0077] Descriptive words are used to describe the nature, state or characteristics of things, such as "efficient", "innovative" and other descriptive words can help us understand the essence or characteristics of things;

[0078] When extracting keywords, first identify which words in the text are closely related to the core content. These words often summarize the theme and main points of the text;

[0079] Domain knowledge can help us identify which words are professional terms in the field, such as "stock", "venture capital", "exchange rate" and other key concepts in the financial field;

[0080] For the extracted keywords, use dictionaries, glossaries or professional synonym tools to find their synonyms and near synonyms;

[0081] Search engine optimization tools can be used to find synonyms and near synonyms of some keywords, such as Google Keyword Planner, which will generate a series of related search terms when you input the article theme-related keywords. These words can be used as synonyms or near synonyms of the keywords, for example: input "healthy diet", it will give "low-sugar diet", "high-fiber food recommendations" and other keyword suggestions;

[0082] Excluding those words irrelevant to the data theme;

[0083] Finding out the relatively important and representative words in a specific article as keywords through the TF-IDF tool, TF (term frequency) represents the frequency of a word appearing in a document, and IDF (inverse document frequency) considers the rarity of the word in the entire document containing the word. By calculating, those words irrelevant to the data theme can be excluded, for example, in an article about football match, the word "football" appears frequently, but since it is common in all sports articles, the IDF is low, so it can be excluded;

[0084] S2, according to the asymmetric encryption algorithm, the important data segment is encrypted by equal length segmentation, the private key of the equal length data segment is symmetrically encrypted, and the quantum key and the one-way hash function are combined to obtain the symmetrically encrypted key;

[0085] The algorithm for encrypting ordinary data segments includes RSA algorithm, ECC algorithm, and the algorithm for encrypting important data segments includes lattice-based cryptography algorithm, ElGamal algorithm, and Paillier algorithm;

[0086] RSA algorithm is based on the difficulty of large integer factorization to ensure security, and is suitable for encrypting ordinary files and general network communication data. Some non-core data transmitted in enterprise internal network, such as office documents and daily reports, although the importance is relatively low, still needs a certain degree of encryption;

[0087] ECC algorithm is a non-symmetric encryption algorithm based on elliptic curve discrete logarithm problem. In mobile application development, it is used to encrypt some non-sensitive configuration information of users, and in Internet of Things device communication, it is used to encrypt ordinary data such as device status report, which can ensure the security of data and will not bring too much calculation burden to the device;

[0088] The security problem of lattice cryptography is based on the difficult problem on the lattice. After the appearance of quantum computer, the traditional lattice problem (like the problem based on RSA and ECC) may be threatened, but lattice cryptography is considered to be able to resist the attack of quantum computer. For important data segments, long-term security is particularly important, and lattice cryptography system can provide such high security guarantee;

[0089] ElGamal algorithm is a non-symmetric encryption algorithm based on discrete logarithm problem, its security is related to the selected parameters and the difficulty of discrete logarithm problem, and the key length is long enough to provide high security, and its encryption process is random enough, the same plaintext will get different ciphertext each time, increasing the difficulty of attack for attackers;

[0090] The Paillier algorithm is an asymmetric encryption algorithm based on the composite residue class problem. In a cloud computing environment, when a cloud service provider needs to perform summation, statistics and other operations on the encrypted data of a user, the algorithm can perform the calculation without leaking the plaintext data, thus protecting the privacy of the user data.

[0091] The method for performing equal-length segmentation on the important data segment according to the selected asymmetric encryption algorithm is as follows:

[0092] Determine the total length L of the important data segment, wherein L is the number of bytes;

[0093] Determine the length S of each sub-segment after equal-length segmentation according to the selected asymmetric encryption algorithm;

[0094] The input data length limit of the lattice-based cryptography algorithm varies with the specific algorithm and implementation. For example, under some typical parameter settings, the plaintext data length can reach several hundred bytes, but this will change with the polynomial degree, modulus and other parameters selected in the key generation process.

[0095] The input data length limit of the ElGamal algorithm is related to the key length. For example, for a key with a security parameter of n bits, the input data length should be less than n-1 bits.

[0096] The Paillier algorithm does not have a strict fixed limit on the input data length based on the key length. For example, in a typical server environment, processing several kilobytes of data is appropriate, depending on the encryption library and parameter settings used.

[0097] Calculate the number of sub-segments after segmentation according to the formula N = L / S.

[0098] Starting from the beginning of the important data segment, divide it into sub-segments with a length of S.

[0099] The specific steps for encrypting the private key by combining symmetric encryption, quantum key, and one-way hash function are as follows:

[0100] The client establishes a quantum communication connection with a trusted quantum key distribution server.

[0101] Generate a set of quantum keys KQ between the client and the server through a quantum key distribution protocol.

[0102] The quantum key distribution protocol is a method for securely distributing keys using quantum mechanics. It is based on principles such as the non-cloning of quantum states and the uncertainty principle. For example, the common BB84 protocol sends quantum bits in different polarization states, and the receiving party randomly measures them. Both parties compare some information through a classical channel, and after filtering out invalid data, they obtain a consistent secure key.

[0103] The client uses a one-way hash function to process the quantum key KQ, and obtains a fixed-length hash value KS as the key for symmetric encryption;

[0104] The one-way hash function has a one-way property, that is, the output can be easily calculated from the input, but it is difficult to deduce the input from the output. By using this property, an initial random seed is used as the input, and the hash value is calculated by iteration, so that a seemingly random sequence can be generated. This sequence has high unpredictability, so it is difficult for an attacker to deduce the next value from the known sequence. Common one-way hash functions include MD5, SHA-1, and SHA-256, etc.

[0105] The generated symmetric encryption key KS is used to encrypt the private key;

[0106] S3, the encrypted data segment is randomly transmitted to the cloud platform server, the cloud platform server performs two hash calculations, and the hash values are returned to the client for comparison. According to the comparison results of the two times, the client decides whether to send the actual data segment combination sequence to the cloud platform server;

[0107] The specific method for the cloud platform server to perform two hash calculations and return to the client for judgment is as follows:

[0108] The client adds an index to the encrypted data segment, and the index is a random natural number and cannot be repeated;

[0109] The client randomly transmits the encrypted data segment to the cloud platform server, records the actual combination sequence X of the encrypted data segment, and combines the index of the transmitted encrypted data segment in the transmission order to obtain a sequence Y. The hash value of the sequence Y is H;

[0110] The cloud platform server receives the encrypted data segment and extracts the index sequence Y1, and obtains the hash value H1. The cloud platform server returns H1 to the client;

[0111] If H1=H, the client adds a number a to the b position of the sequence Y using the Logistic chaotic mapping according to X, calculates the hash value as H2, and returns (a, b) to the cloud platform server;

[0112] The cloud platform server adds (a, b) to the sequence Y1, calculates the hash value as H3 at this time, and returns H3 to the client;

[0113] If H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

[0114] The method for adding a number to the sequence Y using the Logistic chaotic mapping according to X is as follows:

[0115] The original chaotic sequence {x1, x2,..., xn} is obtained by multiple iterations of the Logistic chaotic mapping.

[0116] The original chaotic sequence is normalized according to the formula (xn-min(xn)) / (max(xn)-min(xn)) to obtain the normalized sequence {y1, y2,..., yn}.

[0117] The maximum and minimum values of the sequence X are found as p and q, and the chaotic sequence {z1, z2,..., zn} within the range [q, p] is obtained using the formula zn=q+(p-q)*yn.

[0118] The chaotic sequence {z1, z2,..., zn} is taken out one by one and rounded to the nearest integer, and the first prime number obtained is taken as the inserted number a, and the second prime number is taken as the inserted position b.

[0119] For example, the original data is divided into segments as follows:

[0120] {A7, B3, C11, D1, E9, F5, G2, H8, I4, J10, K6}, and the encrypted sequence sent to the cloud platform server is:

[0121] {G2, E9, A7, D1, I4, K6, C11, H8, B3, F5, J10}, and the actual combination sequence is:

[0122] {7, 5, 1, 4, 9, 11, 3, 8, 2, 6, 10}, the index of the sent sequence is extracted as {2, 9, 7, 1, 4, 6, 11, 8, 3, 5, 10}, and its hash value is calculated and returned to the client. If it is consistent with the client, it needs to be judged for hash conflict, and the original sequence is obtained using the Logistic chaotic mapping as:

[0123] {0.2, 0.624, 0.916032, 0.30477747712, 0.8128684838336, 0.585420596630784, 0.9357276286437184, 0.23670810676846888, 0.7086667564549681, 0.8273600457776834, 0.5508666646456472, 0.9506673798384824, 0.17937270936878858, 0.5804963076839586, 0.9366297647686976}, and it is normalized and controlled within the range of 1-10 to obtain:

[0124] {1, 5.39953488372093, 8.9386328125, 1.889090909090909, 7.784931506849315, 5.084931506849315, 9.224931506849315, 1.2253719008264467, 6.589171974522293, 7.036363636363637, 4.814931506849315, 9.366197183098591, 1, 5.051851851851852, 9.236842105263157}, take the numbers in the above sequence one by one and take the integer, get the first and second prime number, take it as the inserted number and the inserted position, that is (5, 2), send (5, 2) to the cloud platform server, the sequence after insertion is {2, 5, 9, 7, 1, 4, 6, 11, 8, 3, 5, 10}, calculate its hash value and return it to the client, if consistent, the client sends the actual combination sequence to the cloud platform server;

[0125] As Figure 2 A data processing system, comprising: a data segmentation unit, a data encryption unit, a private key encryption unit, a random transmission unit and a hash judgment unit;

[0126] The data segmentation unit mainly segments the data to be transmitted according to the key, divides it into ordinary data segments and important data segments, and transmits the ordinary data segments and important data segments to the data encryption unit;

[0127] The data encryption unit respectively encrypts the ordinary data segments and the important data segments, and according to the selected asymmetric encryption algorithm of the important data segments, continues to divide the important data segments for encryption, obtains a plurality of private keys, and transmits the private keys to the private key encryption unit;

[0128] The private key encryption unit generates a group of keys by quantum key distribution, substitutes the group of keys into a one-way hash function to obtain a hash value, and the hash value is used as the key of symmetric encryption to encrypt the private key, and the encrypted data segment is transmitted to the random transmission unit;

[0129] The random transmission unit randomly labels the encrypted data segment index of the client, randomly transmits the encrypted data segment, obtains sequences X and Y, and calculates the hash value of Y as H, and transmits Y to the hash judgment unit;

[0130] Hash judgment unit, the unit receives the encrypted segmented data of cloud platform server carries out the extraction of index, obtains sequence Y1, obtains the hash value of Y1 as H1, if H=H1, the client uses Logistic chaotic mapping to sequence Y in b position according to X and adds a number a, calculates the hash value as H2, and (a, b) is returned to the cloud platform server, (a, b) is added to the sequence Y1 in the cloud platform server, the hash value as H3 is calculated at this time, and H3 is returned to the client, if H2=H3, the actual combination sequence X of the encrypted data segment is sent to the cloud platform server by the client.

[0131] The part data in the above formula are all dimensionless for numerical calculation, and the contents not described in detail in the specification all belong to the prior art known by the person skilled in the art.

[0132] The above embodiments are only used to illustrate the technical method of the present application but not limit the present application, although the present application is described in detail with reference to the preferred embodiments, the person skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

[0133] The above description shows and describes several preferred embodiments of the present application, but as described before, it should be understood that the present application is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, and can be used in various other combinations, modifications and environments, and can be modified by the above teaching or related art or knowledge within the scope of the inventive concept described herein. The modification and change made by the person skilled in the art without departing from the spirit and scope of the present application should be within the protection scope of the claims of the present application.

Claims

1. A data processing method, characterized in that: Includes the following steps: S1. On the client side, the data is segmented according to the importance of keywords, and important data segments are divided. S2. Encrypt important data segments in equal lengths using an asymmetric encryption algorithm, symmetrically encrypt the private key of the equal-length data segments, and combine the quantum key and a one-way hash function to obtain the key for symmetric encryption. S3. The encrypted data segment is randomly transmitted to the cloud platform server. The cloud platform server performs two hash calculations and returns the hash values ​​to the client for comparison. Based on the comparison results, the client decides whether to send the actual data segment combination sequence to the cloud platform server. The specific method by which the cloud platform server performs two hash calculations and returns the result to the client is as follows: The client adds an index to the encrypted data segment. The index is a random natural number and cannot be repeated. The client randomly transmits encrypted data segments to the cloud platform server and records the actual combination sequence X of the encrypted data segments. At the same time, the indices of the transmitted encrypted data segments are combined in the transmission order to obtain the sequence Y, and the hash value of the sequence Y is calculated as H. The cloud platform server receives the encrypted data segment, extracts the index sequence Y1, calculates the hash value H1, and returns H1 to the client; If H1=H, the client uses Logistic chaotic mapping to add a number a to position b of sequence Y based on X, calculates the hash value H2, and returns (a,b) to the cloud platform server; The cloud platform server adds (a,b) to sequence Y1, calculates the hash value at this time as H3, and returns H3 to the client; If H2=H3, the client will send the actual combination sequence X of the encrypted data segment to the cloud platform server; The method for adding numbers to sequence Y using Logistic chaotic mapping based on X is as follows: The original chaotic sequence {x1,x2,...,xn} is obtained by iterating through the Logistic chaotic map multiple times. The original chaotic sequence is normalized according to the formula (xn-min(xn)) / (max(xn)-min(xn)), and the normalized sequence is {y1,y2,...,yn}. Find the maximum and minimum values ​​of sequence X, p and q, and use the formula zn=q+(pq)*yn to obtain the chaotic sequence {z1,z2,...,zn} in the range [q,p]. Take out the chaotic sequence {z1,z2,...,zn} one by one and round it down to [zn]. Use the first prime number obtained as the number to be inserted, and the second prime number as the position to be inserted, where [] is the rounding symbol. In step S1, the specific method for dividing the data into important data segments is as follows: Create a keyword list containing several keywords that reflect the importance of the content; The entire set of data is scanned line by line. If a part containing the keyword is found, the data segment containing it is marked. For the marked sections containing keywords, expand the range forward or backward to determine the complete boundaries of important segments; After removing the marked and identified important sections, the remaining data content is uniformly classified as ordinary sections.

2. The data processing method according to claim 1, characterized in that: The method for creating a keyword list is as follows: S21: Quickly browse the entire set of data, find words that indicate important content, and for data with clear formatting specifications, focus on the formatted parts and extract keywords from them; S22: Collect action or descriptive words related to important content. For data in a specific field, keywords should be extracted by combining domain knowledge. S23: For the extracted keywords, find their synonyms and near-synonyms; S24: Exclude words that are irrelevant to the data topic.

3. The data processing method according to claim 2, characterized in that: The method for confirming the complete boundaries of important segments is as follows: The important paragraph is defined by tracing back from the important point to the initial point number where it is initially associated with the important point, and extending backward from the important point to the final point number where the important point is fully explained. The boundary between the initial point number where the important point is initially associated with the important point and the final point number where the important point is fully explained is the complete boundary of the important paragraph.

4. The data processing method according to claim 1, characterized in that: In step S2, the algorithms used to encrypt ordinary data segments include the RSA algorithm and the ECC algorithm, while the algorithms used to encrypt important data segments include lattice-based cryptosystem algorithms, the ElGamal algorithm, and the Paillier algorithm.

5. The data processing method according to claim 4, characterized in that: The method for segmenting important data segments into equal-length segments based on the selected asymmetric encryption algorithm is as follows: Determine the total length L of the important data segments, where L is the number of bytes; The length of each segment after equal-length partitioning is determined to be S based on the selected asymmetric encryption algorithm; Calculate the number of segments after partitioning using the formula: N=L / S; Starting from the beginning of the important data segment, divide it according to the determined sub-segment length S.

6. The data processing method according to claim 4, characterized in that: The specific steps for encrypting the private key by combining symmetric encryption, quantum key distribution, and one-way hash functions are as follows: Establish a quantum communication connection between the client and a trusted quantum key distribution server; A set of quantum keys KQ is generated between the client and the server using a quantum key distribution protocol; The client processes the quantum key KQ using a one-way hash function and uses the resulting fixed-length hash value KS as the key for symmetric encryption. The private key is encrypted using the generated symmetric encryption key KS.

7. A data processing system, characterized in that: The data processing method according to any one of claims 1-6 is used as the operating logic of the data processing system; It includes: a data segmentation unit, a data encryption unit, a private key encryption unit, a random transmission unit, and a hash determination unit; The data segmentation unit mainly segments the data to be transmitted according to the keywords, dividing it into ordinary data segments and important data segments, and then transmits the ordinary data segments and important data segments to the data encryption unit. The data encryption unit performs asymmetric encryption on ordinary data segments and important data segments respectively. Based on the asymmetric encryption algorithm selected for the important data segments, it further divides the important data segments into equal lengths for encryption, obtaining multiple sets of private keys, and then transmits the private keys to the private key encryption unit. The private key encryption unit uses quantum key distribution to generate a set of keys, substitutes this set of keys into a one-way hash function to obtain a hash value, and uses this hash value as the key for symmetric encryption to encrypt the private key. The encrypted data segment is then transmitted to the random transmission unit. The random transmission unit randomly labels the index of the encrypted data segment from the client and randomly transmits the encrypted data segment to obtain sequences X and Y. It then calculates the hash value of Y as H and transmits Y to the hash judgment unit. The hash judgment unit extracts the index of the encrypted segmented data received by the cloud platform server to obtain sequence Y1, and calculates the hash value of Y1 as H1. If H=H1, the client adds a number a to position b of sequence Y using Logistic chaotic mapping based on X, calculates the hash value as H2, and returns (a,b) to the cloud platform server. The cloud platform server adds (a,b) to sequence Y1, calculates the hash value at this time as H3, and returns H3 to the client. If H2=H3, the client sends the actual combined sequence X of the encrypted data segments to the cloud platform server.

Images