Data processing system and method

Abstract

The invention belongs to the technical field of data processing, and particularly relates to a data processing system and method, and the method comprises the following steps: S1, segmenting the importance of data at a client according to a keyword, and dividing important data segments; s2, performing equal-length segmented encryption on the important data segment according to an asymmetric encryption algorithm, performing symmetric encryption on a private key of the equal-length data segment, and combining a quantum key with a one-way hash function to obtain a symmetric encrypted key; and S3, the encrypted data segments are randomly transmitted to a cloud platform server, the cloud platform server performs hash calculation twice and returns hash values to the client for comparison, and the client determines whether to send an actual data segment combination sequence to the cloud platform server or not according to comparison results of the two times. According to the invention, the problem of data encryption security in the transmission process from the client to the cloud platform server is solved in practical application.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a data processing system and method. Background Art

[0002] With the rapid development of Internet technology, more and more data needs to be transmitted between clients and cloud platform servers. However, data is easily intercepted and stolen during transmission, which seriously threatens the data security of users. In order to solve this problem, the industry has begun to adopt data encryption technology to encrypt client data to ensure the confidentiality and integrity of data during transmission.

[0003] The application with publication number CN105635144B discloses a data processing method and system based on a cloud platform server. The method first encrypts the client data simply, and then transmits the encrypted data to the cloud platform server. The cloud platform server encrypts the encrypted data again and stores it. The invention improves the security of data stored in the cloud platform server, so that the cloud platform server will not cause user data leakage when it is attacked or invaded. In the above invention, if the client data is transmitted to the cloud platform server and is attacked or invaded by the outside world, it is very easy to cause data leakage, because this stage is closest to the customer's real data, and once leaked, it cannot be recovered. Therefore, it is urgent for technical personnel in this field to solve the above technical problems. Summary of the invention

[0004] In order to solve the problem in the above-mentioned prior art that the data processing system has poor security, if it is invaded by external attacks, it is easy to cause data leakage.

[0005] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0006] A data processing method comprises the following steps:

[0007] S1. Segment the data according to the importance of keywords on the client side to divide the important data segments;

[0008] S2. Encrypt the important data segments in equal length segments according to the asymmetric encryption algorithm, symmetrically encrypt the private keys of the equal length data segments, and combine the quantum key with the one-way hash function to obtain the symmetrically encrypted key;

[0009] S3. Randomly transmit the encrypted data segment to the cloud platform server. The cloud platform server performs two hash calculations and returns the hash value to the client for comparison. Based on the two comparison results, the client decides whether to send the actual data segment combination sequence to the cloud platform server.

[0010] By adopting the above technical solution,

[0011] Furthermore, in step S1, the specific method of dividing the important data segments from the data is:

[0012] Create a keyword list that includes several key words that reflect the importance of the content;

[0013] Scan the entire set of data line by line, and if a part containing keywords is found, mark the data segment where it is located;

[0014] For the marked keyword-containing parts, expand the range forward or backward to determine the complete boundaries of the important segments;

[0015] Except for the important segments that have been marked, the remaining data content is uniformly classified as common segments.

[0016] By adopting the above technical solution,

[0017] Furthermore, the method for creating a keyword list is:

[0018] S21: Quickly browse the entire set of data to find words that indicate important content. For data with clear format specifications, focus on the formatting part and extract keywords from it;

[0019] S22: Collect action or descriptive words related to important content. For data in a specific field, extract keywords based on domain knowledge.

[0020] S23: for the extracted keywords, search for their synonyms and antonyms;

[0021] S24: Exclude words that are not related to the data subject.

[0022] By adopting the above technical solution,

[0023] Furthermore, the method for confirming the complete boundary of the important segment is:

[0024] From the important point, trace back to the initial point number that is initially associated with the important point, and extend from the important point to the ending point number where the important point is explained. The initial point number that is initially associated with the important point to the ending point number where the important point is explained serves as the complete boundary of the important segment.

[0025] By adopting the above technical solution,

[0026] Furthermore, in step S2, the algorithms for encrypting common data segments include RSA algorithm and ECC algorithm, and the algorithms for encrypting important data segments include lattice-based cryptographic algorithm, ElGamal algorithm and Paillier algorithm.

[0027] By adopting the above technical solution,

[0028] Furthermore, the method of dividing the important data segments into equal-length segments according to the selected asymmetric encryption algorithm is as follows:

[0029] Determine the total length L of the important data segment, where L is the number of bytes;

[0030] According to the selected asymmetric encryption algorithm, the length of each sub-segment after equal length division is determined to be S;

[0031] According to the formula: N = L / S, calculate the number of sub-segments after division;

[0032] Starting from the beginning of the important data segment, it is divided according to the determined sub-segment length S.

[0033] By adopting the above technical solution,

[0034] Furthermore, the specific steps of combining symmetric encryption, quantum key, and one-way hash function to encrypt the private key are as follows:

[0035] Establish a quantum communication connection between the client and a trusted quantum key distribution server;

[0036] Through the quantum key distribution protocol, a set of quantum keys KQ is generated between the client and the server;

[0037] The client uses a one-way hash function to process the quantum key KQ and uses the fixed-length hash value KS as the key for symmetric encryption.

[0038] The private key is encrypted using the generated symmetric encryption key KS.

[0039] By adopting the above technical solution,

[0040] Further, in step S3, the specific method in which the cloud platform server performs two hash calculations and returns the result to the client for judgment is:

[0041] S31: The client adds a subscript to the encrypted data segment. The subscript is a random natural number and cannot be repeated.

[0042] S32: The client randomly transmits the encrypted data segment to the cloud platform server, and records the actual combination sequence X of the encrypted data segment. At the same time, the subscripts of the transmitted encrypted data segment are combined in the transmission order to obtain the sequence Y, and the hash value of the sequence Y is obtained as H;

[0043] S33: The cloud platform server receives the encrypted data segment and extracts the subscript sequence Y1, obtains the hash value H1, and returns H1 to the client;

[0044] S34: If H1=H, the client adds a number a to the b position of the sequence Y using the Logistic chaotic map according to X, calculates the hash value as H2, and returns (a, b) to the cloud platform server;

[0045] S35: The cloud platform server adds (a, b) to the sequence Y1, calculates the hash value at this time as H3, and returns H3 to the client;

[0046] S36: If H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

[0047] Furthermore, the method of adding numbers to sequence Y using Logistic chaotic mapping according to X is:

[0048] The original chaotic sequence {x1,x2,...,xn} is obtained by multiple iterations of Logistic chaotic mapping;

[0049] 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};

[0050] Find the maximum and minimum values ​​of sequence X as p, q, and use the formula zn=q+(pq)*yn to get the chaotic sequence {z1,z2,...,zn} in the range [q,p];

[0051] Take out the chaotic sequence {z1,z2,...,zn} one by one and round it to [zn], use the first prime number obtained as the inserted number a, and the second prime number as the insertion position b, where [] is the rounding symbol.

[0052] The present invention further discloses a data processing system, using the data processing method as the operation logic of the data processing system;

[0053] It includes: data segmentation unit, data encryption unit, private key encryption unit, random transmission unit and hash judgment unit;

[0054] The data segmentation unit mainly performs segmentation operation on the data to be transmitted according to the keywords, divides the data into common data segments and important data segments, and transmits the common data segments and the important data segments to the data encryption unit;

[0055] The data encryption unit performs asymmetric encryption on the common data segment and the important data segment respectively, and continues to divide the important data segment into equal lengths for encryption according to the asymmetric encryption algorithm selected for the important data segment, obtains multiple groups of private keys, and transmits the private keys to the private key encryption unit;

[0056] The private key encryption unit generates a set of keys using quantum key generation, substitutes the set of keys into a one-way hash function to obtain a hash value, uses the hash value as the key for symmetric encryption to encrypt the private key, and transmits the encrypted data segment to the random transmission unit;

[0057] The random transmission unit randomly labels the subscript of the encrypted data segment of the client, and randomly transmits the encrypted data segment to obtain the sequence X and Y, and obtains the hash value of Y as H, and transmits Y to the hash judgment unit;

[0058] The hash judgment unit extracts the subscript of the encrypted segmented data received by the cloud platform server to obtain the sequence Y1, and 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 map, 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 at this time as H3, 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.

[0059] The present invention has at least the following beneficial effects:

[0060] 1. The present invention uses an asymmetric encryption algorithm to encrypt important data segments in equal length segments, and combines quantum keys with one-way hash functions to protect private keys, thereby ensuring high security of data during transmission from the client to the cloud platform server. The random transmission order and the use of hash values ​​to verify data integrity further prevent the possibility of data being intercepted or tampered with midway, thereby improving the security of data transmission and effectively resisting the risks of external attacks and data leakage.

[0061] 2. The present invention identifies and encrypts important data segments based on keywords, rather than encrypting all data with the same strength. This ensures that sensitive information is fully protected, reduces unnecessary computing overhead, and improves overall data processing efficiency. The selection and application of different types of asymmetric encryption algorithms (such as RSA, ECC), as well as the use of higher-level encryption algorithms for important data segments (such as lattice-based cryptographic systems, ElGamal, Paillier), demonstrates the high flexibility and adaptability of the solution.

[0062] 3. The present invention 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. The client uses Logistic chaotic mapping to generate additional verification parameters (a, b), 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 both parties with a reliable means of data integrity and authenticity verification, thereby ensuring the accuracy of data during transmission, effectively preventing potential data tampering risks, and improving the credibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is the principle diagram of the present invention;

[0064] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] like Figure 1 , a data processing method, comprising:

[0067] S1. Segment the data according to the importance of keywords on the client side to divide the important data segments;

[0068] The specific method of dividing important data segments from data is:

[0069] Create a keyword list that includes several key words that reflect the importance of the content;

[0070] Scan the entire set of data line by line, and pay attention to whether the words in the keyword list appear;

[0071] If a part containing keywords is found, the data segment containing the keyword is marked;

[0072] For the marked keyword-containing sections, extend the range forward or backward to determine the complete boundaries of the important segments;

[0073] The scope of extension forward or backward to determine the complete boundary of the important paragraph should be based on the principle of semantic integrity and logical coherence, and should be extended forward to the initial point number where the important point is initially associated, and extended backward to the end point number where the important point is explained;

[0074] The initial punctuation mark is generally a "semicolon", "period", "colon" or the like, which is used to indicate a sentence or a new paragraph of content to be elaborated;

[0075] The ending punctuation mark is usually a period, which is used to indicate that the elaboration of this important paragraph has ended;

[0076] For example, if the sentence with keywords is "Key indicators: market share reached 30%, thanks to our efforts in channel expansion and product optimization in the first half of the year", you can trace back to the complete sentence: Looking back on this year, our team has been very hardworking: In the first half of the year, we focused on a series of work to improve product competitiveness and market share. Keep going until you find the ":" before "first half of the year", and include "In the first half of the year, we focused on a series of work to improve product competitiveness and market share" in the important paragraph, and trace back to the place where "." appears, and include the part describing the specific measures for channel expansion and product optimization;

[0077] Excluding the marked important segments, the remaining data content is uniformly classified as common segments;

[0078] To create a keyword list:

[0079] Quickly scan the entire set of data to find words that indicate important content. For data with clear formatting specifications, focus on these formatting parts first and extract keywords from them;

[0080] Words that are intuitively used to indicate importance include: key, important, core, necessary, significant, crucial, pivotal, indispensable, main, fundamental, priority, prominent, substantial, decisive, significant, etc.

[0081] 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;

[0082] Find action or descriptive words related to important content. For data in a specific field, combine domain knowledge to extract keywords.

[0083] Action words usually indicate a certain behavior, process or activity. For example, in some news reports, "increase", "decrease" and "improvement" are all action words used to describe the core actions of news events.

[0084] Descriptive words are used to describe the nature, state or characteristics of things. Descriptive words such as "efficient" and "innovative" can help us understand the essence or characteristics of things.

[0085] When extracting keywords, we must first identify which words in the text are closely related to the core content. These words can often summarize the theme and key points of the text.

[0086] Domain knowledge can help us identify which words are professional terms in the field. For example, "stocks", "venture capital", "exchange rates" and so on in the financial field are all key concepts.

[0087] For the extracted keywords, use dictionaries, vocabularies or professional synonym tools to find their synonyms and antonyms;

[0088] You can use search engine optimization tools to find synonyms and antonyms of some keywords, such as Google Keyword Planner. Enter keywords related to the article topic in it, and it will generate a series of related search terms. These words can be used as synonyms or antonyms of keywords. For example, if you enter "healthy diet", it will give keyword suggestions such as "low-sugar diet" and "high-fiber food recommendation".

[0089] Exclude words that are not relevant to the subject of the data;

[0090] Use the TF-IDF tool to find relatively important and representative words in a specific article as keywords. TF (term frequency) indicates how often a word appears in a document, and IDF (inverse document frequency) considers the rarity of the word in the entire document containing the word. Through calculation, words that are not related to the data topic can be eliminated. For example, in an article about football games, the word "football" appears very frequently, but because it is common in all sports articles, the IDF is very low, so it can be eliminated.

[0091] S2. Encrypt the important data segments in equal length segments according to the asymmetric encryption algorithm, symmetrically encrypt the private keys of the equal length data segments, and combine the quantum key with the one-way hash function to obtain the symmetrically encrypted key;

[0092] The algorithms used to encrypt common data segments include RSA algorithm and ECC algorithm, and the algorithms used to encrypt important data segments include lattice-based cryptographic algorithm, ElGamal algorithm, and Paillier algorithm.

[0093] The RSA algorithm ensures security based on the difficulty of large integer decomposition problems. It is suitable for encrypting ordinary files and general network communication data. For example, some non-core data transmitted in the enterprise internal network, such as office documents and daily reports, although relatively less important, still requires a certain degree of encryption;

[0094] The ECC algorithm is an asymmetric encryption algorithm based on the elliptic curve discrete logarithm problem. In mobile application development, it is used to encrypt some non-sensitive configuration information of users. In IoT device communication, it encrypts common data such as device status reports, which can ensure data security without bringing too much computing burden to the device.

[0095] Lattice cryptography security issues are difficult problems based on lattices. After the emergence of quantum computers, traditional number theory-based problems (such as those based on RSA and ECC) may be threatened, but lattice cryptography is believed to be able to resist attacks from quantum computers. For important data segments, their long-term security is particularly important, and the lattice cryptography system can provide such high security guarantees.

[0096] The ElGamal algorithm is an asymmetric encryption algorithm based on the discrete logarithm problem. Its security is related to the selected parameters and the difficulty of the discrete logarithm problem. A sufficiently long key length can provide high security, and its encryption process is sufficiently random. Each time the same plaintext is encrypted, a different ciphertext will be obtained, which increases the difficulty for attackers to crack it.

[0097] The Paillier algorithm is an asymmetric encryption algorithm based on the composite remainder class problem. In a cloud computing environment, when a cloud service provider needs to perform operations such as summation and statistics on a user's encrypted data, the algorithm can perform calculations without leaking plaintext data, thus protecting the privacy of user data.

[0098] The method of dividing important data segments into equal-length segments according to the selected asymmetric encryption algorithm is as follows:

[0099] Determine the total length L of the important data segment, where L is the number of bytes;

[0100] According to the selected asymmetric encryption algorithm, the length of each sub-segment after equal length division is determined to be S;

[0101] The input data length limit of lattice-based cryptographic algorithms varies depending on 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 changes in parameters such as the degree of the polynomial and the modulus selected during the key generation process;

[0102] 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;

[0103] The Paillier algorithm does not have a strict fixed limit on the input data length based on the key length. For example, in a normal server environment, it is suitable to process several thousand bytes of data, depending on the encryption library used and the parameter settings;

[0104] Calculate the number of sub-segments after division according to the formula N=L / S;

[0105] Starting from the beginning of the important data segment, it is divided according to the determined sub-segment length S.

[0106] The specific steps of combining symmetric encryption, quantum key, and one-way hash function to encrypt the private key are:

[0107] The client establishes a quantum communication connection with a trusted quantum key distribution server;

[0108] Through the quantum key distribution protocol, a set of quantum keys KQ is generated between the client and the server;

[0109] The quantum key distribution protocol is a method that uses the characteristics of quantum mechanics to achieve secure key distribution. It is mainly based on the principles of non-cloning and uncertainty of quantum states. For example, in the common BB84 protocol, the sender sends quantum bits in different polarization states, and the receiver measures them randomly. The two parties compare partial information through the classical channel and obtain a consistent security key after filtering out invalid data.

[0110] The client uses a one-way hash function to process the quantum key KQ and uses the fixed-length hash value KS as the key for symmetric encryption.

[0111] One-way hash functions are one-way, that is, the output can be easily calculated from the input, but it is difficult to infer the input from the output. By using this feature, an initial random seed is used as the input, and the hash value is continuously calculated iteratively to generate a seemingly random sequence. This sequence is highly unpredictable, so it is difficult for an attacker to deduce the next value from a known sequence. Common one-way hash functions include MD5, SHA-1, SHA-256, etc.

[0112] Encrypt the private key using the generated symmetric encryption key KS;

[0113] S3, randomly transmit the encrypted data segment to the cloud platform server, the cloud platform server performs two hash calculations, and returns the hash value to the client for comparison. Based on the two comparison results, the client decides whether to send the actual data segment combination sequence to the cloud platform server;

[0114] The specific method by which the cloud platform server performs two hash calculations and returns the judgment to the client is:

[0115] The client adds a subscript to the encrypted data segment. The subscript is a random natural number and cannot be repeated.

[0116] 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 subscripts 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 obtained as H;

[0117] The cloud platform server receives the encrypted data segment and extracts the subscript sequence Y1, obtains the hash value H1, and returns H1 to the client;

[0118] If H1=H, the client uses Logistic chaotic mapping to add a number a to the b position of sequence Y according to X, calculates the hash value as H2, and returns (a, b) to the cloud platform server;

[0119] The cloud platform server adds (a, b) to the sequence Y1, calculates the hash value at this time as H3, and returns H3 to the client;

[0120] If H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

[0121] The method of adding numbers to sequence Y using Logistic chaotic mapping according to X is:

[0122] The original chaotic sequence {x1,x2,...,xn} is obtained by multiple iterations of Logistic chaotic mapping;

[0123] 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};

[0124] Find the maximum and minimum values ​​of sequence X as p, q, and use the formula zn=q+(pq)*yn to get the chaotic sequence {z1,z2,...,zn} in the range [q,p];

[0125] Take out the chaotic sequence {z1,z2,...,zn} one by one, and round it to [zn], and use the first prime number obtained as the inserted number a, and the second prime number as the inserted position b, where [] is the rounding symbol;

[0126] For example, the original data after segmentation is:

[0127] {A7,B3,C11,D1,E9,F5,G2,H8,I4,J10,K6}, the sequence randomly sent to the cloud platform server after encryption is:

[0128] {G2,E9,A7,D1,I4,K6,C11,H8,B3,F5,J10}, the actual combination sequence is:

[0129] {7,5,1,4,9,11,3,8,2,6,10}, extract the sending sequence subscript {2,9,7,1,4,6,11,8,3,5,10}, calculate its hash value and return it to the client. If it is consistent with the client, it is necessary to determine the hash conflict. The original set of sequences is obtained by using Logistic chaotic mapping:

[0130] {0.2,0.624,0.916032,0.30477747712,0.8128684838336,0.585420596630784,0.9357276286437184,0.23670810676846888,0.7086667564549681,0.82736 00457776834,0.5508666646456472,0.9506673798384824,0.17937270936878858,0.5804963076839586,0.9366297647686976}, normalize it and control it within the range of 1-10, and get

[0131] {1,5.39953488372093,8.9386328125,1.88909090909090909,7.784931506849315,5.084931506849315,9.224931506849315,1.2253719008264467,6.589171974522293,7.036363636363637,4.814931506849315,9.366197183 098591,1,5.051851851851852,9.236842105263157}, take out the numbers in the above sequence one by one and round them up to get the first and second prime numbers, use them as the inserted numbers and insertion positions, 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 it is consistent, the client sends the actual combination sequence to the cloud platform server;

[0132] like 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;

[0133] A data segmentation unit, which mainly segments the data to be transmitted according to the keyword, divides the data into common data segments and important data segments, and transmits the common data segments and the important data segments to the data encryption unit;

[0134] A data encryption unit, which performs asymmetric encryption on the common data segment and the important data segment respectively, and further divides the important data segment into equal lengths according to the asymmetric encryption algorithm selected for the important data segment to encrypt, obtains multiple groups of private keys, and transmits the private keys to the private key encryption unit;

[0135] A private key encryption unit, which uses quantum key generation to generate a set of keys, substitutes this set of keys into a one-way hash function to obtain a hash value, uses this hash value as a symmetric encryption key to encrypt the private key, and transmits the encrypted data segment to the random transmission unit;

[0136] A random transmission unit, which randomly numbers the subscripts of the encrypted data segments of the client, and randomly transmits the encrypted data segments to obtain sequences X and Y, and obtains the hash value of Y as H, and transmits Y to the hash judgment unit;

[0137] The hash judgment unit extracts the subscript of the encrypted segmented data received by the cloud platform server to obtain the sequence Y1, and 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 map, 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 at this time as H3, 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.

[0138] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0139] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

[0140] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention concept described herein through the above teachings or the technology or knowledge of the relevant field. Changes and variations made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A data processing method, characterized in that: The following steps are involved: S1. Segment the data according to the importance of keywords on the client side to divide the important data segments; S2. Encrypt the important data segments in equal length segments according to the asymmetric encryption algorithm, symmetrically encrypt the private keys of the equal length data segments, and combine the quantum key with the one-way hash function to obtain the symmetrically encrypted key; S3. Randomly transmit the encrypted data segment to the cloud platform server. The cloud platform server performs two hash calculations and returns the hash value to the client for comparison. Based on the two comparison results, the client decides whether to send the actual data segment combination sequence to the cloud platform server.

2. The data processing method according to claim 1, characterized in that: In step S1, the specific method of dividing the important data segments from the data is: Create a keyword list that includes several key words that reflect the importance of the content; Scan the entire set of data line by line, and if a part containing keywords is found, mark the data segment where it is located; For the marked keyword-containing parts, expand the range forward or backward to determine the complete boundaries of the important segments; Except for the important segments that have been marked, the remaining data content is uniformly classified as common segments.

3. The data processing method according to claim 2, characterized in that: To create a keyword list: S21: Quickly browse the entire set of data to find words that indicate important content. For data with clear format specifications, focus on the formatting part and extract keywords from it; S22: Collect action or descriptive words related to important content. For data in a specific field, extract keywords based on domain knowledge. S23: for the extracted keywords, search for their synonyms and antonyms; S24: Exclude words that are not related to the data subject.

4. The data processing method according to claim 3, characterized in that: The method to confirm the complete boundaries of important segments is: From the important point, trace back to the initial point number that is initially associated with the important point, and extend from the important point to the ending point number where the important point is explained. The initial point number that is initially associated with the important point to the ending point number where the important point is explained serves as the complete boundary of the important segment.

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

6. The data processing method according to claim 5, characterized in that: The method of dividing important data segments into equal-length segments according to the selected asymmetric encryption algorithm is as follows: Determine the total length L of the important data segment, where L is the number of bytes; According to the selected asymmetric encryption algorithm, the length of each sub-segment after equal length division is determined to be S; According to the formula: N = L / S, calculate the number of sub-segments after division; Starting from the beginning of the important data segment, it is divided according to the determined sub-segment length S.

7. The data processing method according to claim 5, characterized in that: The specific steps of combining symmetric encryption, quantum key, and one-way hash function to encrypt the private key are: Establish a quantum communication connection between the client and a trusted quantum key distribution server; Through the quantum key distribution protocol, a set of quantum keys KQ is generated between the client and the server; The client uses a one-way hash function to process the quantum key KQ and uses the fixed-length hash value KS as the key for symmetric encryption. The private key is encrypted using the generated symmetric encryption key KS.

8. The data processing method according to claim 1, characterized in that: In step S3, the specific method in which the cloud platform server performs two hash calculations and returns the result to the client is: S31: The client adds a subscript to the encrypted data segment. The subscript is a random natural number and cannot be repeated. S32: The client randomly transmits the encrypted data segment to the cloud platform server, and records the actual combination sequence X of the encrypted data segment. At the same time, the subscripts of the transmitted encrypted data segment are combined in the transmission order to obtain the sequence Y, and the hash value of the sequence Y is obtained as H; S33: The cloud platform server receives the encrypted data segment and extracts the subscript sequence Y1, obtains the hash value H1, and returns H1 to the client; S34: If H1=H, the client adds a number a to the b position of the sequence Y using the Logistic chaotic map according to X, calculates the hash value as H2, and returns (a, b) to the cloud platform server; S35: The cloud platform server adds (a, b) to the sequence Y1, calculates the hash value at this time as H3, and returns H3 to the client; S36: If H2=H3, the client sends the actual combination sequence X of the encrypted data segment to the cloud platform server.

9. The data processing method according to claim 8, characterized in that: The method of adding numbers to sequence Y using Logistic chaotic mapping according to X is: The original chaotic sequence {x1,x2,...,xn} is obtained by multiple iterations of Logistic chaotic mapping; 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 as p, q, and use the formula zn=q+(pq)*yn to get 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 to [zn], use the first prime number obtained as the inserted number a, and the second prime number as the insertion position b, where [] is the rounding symbol.

10. A data processing system, characterized in that: Using the data processing method according to any one of claims 1 to 9 as the operating logic of the data processing system; It includes: data segmentation unit, data encryption unit, private key encryption unit, random transmission unit and hash judgment unit; The data segmentation unit mainly performs segmentation operation on the data to be transmitted according to the keywords, divides the data into common data segments and important data segments, and transmits the common data segments and the important data segments to the data encryption unit; The data encryption unit performs asymmetric encryption on the common data segment and the important data segment respectively, and continues to divide the important data segment into equal lengths for encryption according to the asymmetric encryption algorithm selected for the important data segment, obtains multiple groups of private keys, and transmits the private keys to the private key encryption unit; The private key encryption unit generates a set of keys using quantum key generation, substitutes the set of keys into a one-way hash function to obtain a hash value, uses the hash value as the key for symmetric encryption to encrypt the private key, and transmits the encrypted data segment to the random transmission unit; The random transmission unit randomly labels the subscript of the encrypted data segment of the client, and randomly transmits the encrypted data segment to obtain the sequence X and Y, and obtains the hash value of Y as H, and transmits Y to the hash judgment unit; The hash judgment unit extracts the subscript of the encrypted segmented data received by the cloud platform server to obtain the sequence Y1, and 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 map, 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 at this time as H3, 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.

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