A method and system for safely sharing ocean big data based on dynamic data tracing

Abstract

The present invention discloses a method and system for safely sharing ocean big data based on dynamic data tracing, which relates to the technical field of ocean big data. The system uses a preliminary analysis module between data to construct a correlation coefficient through correlation analysis, which can accurately identify and screen out data parameters with high correlation and potential sensitivity. This process can preliminarily identify key parameters that may cause privacy leakage risks before data sharing, providing targeted data security protection. When the security sharing module performs a combination analysis on the screened sensitive parameters, it constructs a discrete degree coefficient and compares it with a preset security range to effectively evaluate the privacy risks that may exist after the data is combined. This mechanism can analyze the distribution changes after the data is combined in real time and dynamically, ensuring the security of the data during the sharing process.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean big data, and in particular to a method and system for securely sharing ocean big data based on dynamic data tracing. Background Art

[0002] As an advanced technology in the field of big data analysis, data dynamic tracing is mainly used to track and record the flow path of data to ensure the traceability and credibility of the data source. Driven by this technology, the safe sharing of marine big data has become a specific application field that has attracted much attention. Marine big data involves a wide range of multi-dimensional information such as hydrology, meteorology and biology. These data come from a wide range of sources and are huge in quantity, providing important support for marine environmental monitoring, climate change research and resource management. In the field of dynamic data tracing, by using a variety of sensors and monitoring systems, real-time collection of ocean status data, and dynamic tracking and tracing of data have become important means to improve data credibility and reliability. Specifically for the safe sharing of marine big data, how to ensure the effective use and sharing of data while protecting sensitive information has become a key issue that needs to be solved urgently.

[0003] At present, the sharing process of ocean big data faces multiple challenges. First, in the existing sharing system, there is often a lack of sufficient analysis of data relevance and sensitivity. Some seemingly harmless data may lead to the leakage of sensitive information after combination. Traditional data processing and sharing methods are difficult to effectively deal with this hidden risk, so it is necessary to explore the privacy issues hidden in ocean big data. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for securely sharing ocean big data based on dynamic data tracing, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a marine big data security sharing system based on dynamic data tracing, including a data collection module, a data processing module, a preliminary data analysis module, a security sharing module and a compensation module;

[0006] The data collection module is used to monitor the ocean state in real time using multiple sets of monitoring instruments to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set;

[0007] The data processing module is used to filter and correct the collected raw data, remove outliers, and extract key data, and classify and count the extracted data to obtain various parameter sequence sets, and at the same time use blockchain technology to trace and verify each parameter sequence set;

[0008] The data preliminary analysis module is used to perform correlation analysis on each parameter sequence set after classification to construct a correlation coefficient Glxs, and preliminarily screen sensitive parameters according to the value of the correlation coefficient Glxs;

[0009] The security sharing module is used to perform a combination analysis on the sensitive parameters based on the preliminary screening of sensitive parameters, and after the combination, analyze the distribution changes of each combination pair to construct the discrete degree coefficient of each combination pair. , and pre-set the safety range , by dividing the dispersion coefficient of each combination pair by and safety range Conduct comparative analysis to determine whether compensatory measures are necessary and, if necessary, issue a correction order;

[0010] The compensation module is used to, after receiving the compensation command, calculate the discrete degree coefficient of the corresponding combination pair. , construct the compensation index Bczs, and combine it with the safety range , introducing noise compensation.

[0011] Preferably, the data collection module is used to monitor and collect ocean data sets in real time from multiple groups of ocean sensors, ocean observation satellites and ship-borne weather stations, wherein the ocean data sets include relevant hydrological data, relevant meteorological data and relevant biological data in the ocean.

[0012] Preferably, the data processing module includes a cleaning unit, a classification unit and a data tracing unit;

[0013] The cleaning unit is used to identify and remove duplicate values ​​and abnormal values ​​in the ocean data set, and perform feature extraction on the ocean data set to reduce data dimensions and remove redundant information;

[0014] The classification unit is used to classify the relevant hydrological data, relevant meteorological data and relevant biological data in the ocean data set, extract the parameters in different monitoring periods, and establish each parameter sequence set respectively;

[0015] The data tracing unit is used to generate a unique hash value for each parameter in each parameter sequence set using blockchain technology, and automatically record the visitor's operation record each time the data is accessed or shared, wherein the operation record content includes the data identifier, operation type, timestamp and operation user.

[0016] Preferably, the data preliminary analysis module includes a mutual impact analysis unit and a screening unit;

[0017] The mutual influence analysis unit is used to perform correlation analysis on each parameter sequence set after classification to construct the correlation coefficient Glxs between the corresponding parameters, which is specifically obtained in the following manner:

[0018] ;

[0019] In the formula, X and Y are two random parameters in the set of all parameter sequences; It is the joint probability distribution of parameter 1 X and parameter 2 Y; is the marginal probability distribution of parameter X; is the marginal probability distribution of the second parameter Y.

[0020] Preferably, the screening unit is used to calculate the correlation coefficients Glxs between several groups of parameters according to the method of obtaining the correlation coefficients Glxs between the corresponding parameters in the mutual influence analysis unit, and obtain the average correlation coefficients Glxs between the parameters through a statistical algorithm. and the standard deviation of the correlation coefficient between parameters , based on the average correlation coefficient between parameters and the standard deviation of the correlation coefficient between parameters , set the threshold G, which is set in the following way: ; k is represented as a constant;

[0021] By comparing the correlation coefficient Glxs between the corresponding parameters with the threshold G, sensitive parameters can be preliminarily screened out. The specific screening contents are as follows:

[0022] If the correlation coefficient Glxs between the corresponding parameters exceeds the threshold G, it means that there is a correlation risk between the corresponding parameters. At this time, the sensitive parameters will be preliminarily marked and screened out;

[0023] If the correlation coefficient Glxs between the corresponding parameters does not exceed the threshold G, it means that there is no correlation risk between the corresponding parameters, and no marking and screening will be performed at this time.

[0024] Preferably, the security sharing module includes a combination unit, a combination analysis unit and an identification unit;

[0025] The combination unit is used to perform statistics on the sensitive parameters screened in the screening unit, and combine several groups of sensitive parameters in combination with probability theory to obtain several groups of combination pairs.

[0026] Preferably, the combination analysis unit is used to analyze the distribution changes of each combination pair to construct a dispersion coefficient of each combination pair. , which can be obtained by: ;

[0027] In the formula, Z is a sensitive parameter. is the probability density of uncombined sensitive information; is the probability density of the combined sensitive information; It represents a directionality, i.e., the divergence of the uncombined sensitive information relative to the combined sensitive information.

[0028] Preferably, the identification unit is used to separate the discrete degree coefficients of each combination pair Through statistical algorithms, the average dispersion coefficient of the combination pairs is obtained respectively. and the standard deviation of the coefficient of dispersion of the combination , by combining the average dispersion coefficient of the pair and the standard deviation of the coefficient of dispersion of the combination , set the safety range , set it as follows:

[0029] ; ;

[0030] Where A is a constant;

[0031] By using the dispersion coefficient of each combination pair and safety range Comparative analysis is conducted to determine whether compensation measures need to be introduced. The specific determination is as follows:

[0032] If the dispersion coefficient of the corresponding combination pair Falling into the safe range When the amount is within the limit, it is determined that no compensatory measures are necessary;

[0033] If the dispersion coefficient of the corresponding combination pair Not within the safety range If it is determined that compensatory measures are necessary, a correction order will be issued externally.

[0034] Preferably, the compensation module includes a compensation analysis unit and a correction unit;

[0035] The compensation analysis unit is used to introduce noise into the sensitive parameters involved in the corresponding combination pair after receiving the compensation command, and combine the discrete degree coefficient of the corresponding combination pair , construct the corresponding compensation index Bczs, which can be obtained in the following way:

[0036] ;

[0037] In the formula, Indicates the discrete degree coefficient of each input combination pair When any record in changes, the maximum value of the output change; c is represented by the demand constant;

[0038] The correction unit is used to compensate and analyze the sensitive parameters after the noise is introduced, combined with the safety range , by comparing the corresponding compensation index Bczs again to see if it falls within the safe range To determine whether it is necessary to continue the noise introduction operation, if the corresponding compensation index Bczs falls within the safe range , then stop the noise introduction operation.

[0039] A method for safely sharing ocean big data based on dynamic data tracing includes the following steps:

[0040] Step 1: First, use multiple sets of monitoring instruments to monitor the ocean state in real time to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set;

[0041] Step 2: Filter and correct the collected raw data, remove outliers, extract key data, and classify and count the extracted data to obtain each parameter sequence set. At the same time, use blockchain technology to trace and verify each parameter sequence set.

[0042] Step 3: Then, the classified parameter sequence sets are subjected to correlation analysis to construct the correlation coefficient Glxs, and sensitive information is preliminarily screened according to the value of the correlation coefficient Glxs;

[0043] Step 4: After the sensitive information is initially screened out, the sensitive information is combined and analyzed. After the combination, the distribution changes of each combination pair are analyzed to construct the discrete degree coefficient Lcxs of each combination pair, and the security threshold G is pre-set. By comparing and analyzing the discrete degree coefficient Lcxs of each combination pair with the security threshold G, it is determined whether compensation measures need to be introduced. If necessary, a correction command is issued;

[0044] Step 5: Finally, after receiving the compensation command, the compensation index Bczs is constructed according to the discrete degree coefficient Lcxs of the corresponding combination pair, and noise compensation is introduced in combination with the safety threshold G.

[0045] The present invention provides a method and system for safely sharing ocean big data based on dynamic data tracing, which has the following beneficial effects:

[0046] (1) The system uses the preliminary analysis module between data to construct the correlation coefficient Glxs through correlation analysis, which can accurately identify and screen out data parameters with high correlation and potential sensitivity. This process can preliminarily identify key parameters that may cause privacy leakage risks before data sharing, providing targeted data security protection. When the security sharing module conducts combined analysis on the screened sensitive parameters, it constructs a discrete degree coefficient And with the preset safety range The comparison effectively evaluates the privacy risks that may exist after the data is combined. This mechanism can analyze the distribution changes after the data is combined in real time and dynamically to ensure the security of the data during the sharing process. and safety range The analysis results show that by constructing the compensation index Bczs and introducing noise compensation, the risk of privacy leakage in the data sharing process is effectively reduced. This adaptive noise compensation mechanism can further balance the availability and privacy of data, thereby enhancing the security performance of the system. In short, the system further improves the security and privacy protection level of marine big data in the sharing process through multi-level analysis and compensation mechanisms, thereby ensuring the safe sharing of sensitive information while retaining the validity and practical value of the data as much as possible.

[0047] (2) Through the mutual influence analysis unit in the preliminary analysis module between data, the system can accurately analyze the correlation of each parameter sequence set and construct the correlation coefficient Glxs between parameters. With the help of this coefficient, the system can deeply understand the potential correlation between different parameters in marine big data, especially the calculation of the joint probability of specific parameters such as temperature and salinity in the same monitoring period. This analysis method helps to accurately identify the correlation risk between data parameters and provides a scientific basis for subsequent data processing and decision-making. The screening unit calculates the average correlation coefficient and standard deviation between several groups of parameters through statistical algorithms based on the correlation coefficient Glxs obtained in the correlation analysis, and sets the screening threshold G based on these statistical indicators. By comparing the correlation coefficient Glxs with the set threshold G, the system can quickly screen out sensitive parameters and mark the combination pairs with correlation risks. In this way, in massive data, the system can effectively filter out sensitive parameters that pose a potential threat to data security and privacy, thereby further improving the efficiency of data analysis and processing.

[0048] (3) The combination unit in the security sharing module collects statistics on the selected sensitive parameters and combines them with probability theory to obtain several groups of sensitive parameter combination pairs. This process can combine and analyze the multi-dimensional characteristics of sensitive data and reveal the distribution characteristics of the combined data. The combination analysis unit further constructs the discrete degree coefficient of each combination pair by analyzing the distribution changes of the combination pairs. This precise analysis method helps the system understand the impact of different parameter combinations on data distribution and provides data support for subsequent safety measures. , and then evaluate the distribution changes after data combination. The system is based on the set safety range Coefficient of dispersion Comparative analysis can monitor the security status of data combinations in real time. If the discrete coefficient of the combination exceeds the safe range, the system will automatically issue a correction command. This dynamic adjustment mechanism ensures that the system can respond to potential security threats immediately and adjust data processing strategies in real time, thereby enhancing the security and flexibility of the system. By comparing the discrete coefficient with the safe range, the system can accurately identify which combinations pose privacy risks and introduce compensatory measures when necessary.

[0049] (4) After receiving the compensation command, the compensation analysis unit introduces noise into the sensitive parameters and constructs the compensation index Bczs in combination with the discrete degree coefficient. This approach effectively increases the strength of privacy protection, thereby effectively concealing the true value of sensitive data in data queries. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a block diagram of a marine big data security sharing system based on dynamic data tracing of the present invention;

[0051] Figure 2 The present invention is a flow chart of a method for securely sharing ocean big data based on dynamic data tracing. DETAILED DESCRIPTION

[0052] 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.

[0053] Example 1

[0054] See also Figure 1 The present invention provides a marine big data security sharing system based on dynamic data tracing, including a data collection module, a data processing module, a preliminary data analysis module, a security sharing module and a compensation module;

[0055] The data collection module is used to monitor the ocean state in real time using multiple sets of monitoring instruments to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set;

[0056] The data processing module is used to filter and correct the collected raw data, remove outliers, and extract key data, and classify and count the extracted data to obtain various parameter sequence sets, and at the same time use blockchain technology to trace and verify each parameter sequence set;

[0057] The data preliminary analysis module is used to perform correlation analysis on each parameter sequence set after classification to construct a correlation coefficient Glxs, and preliminarily screen sensitive parameters according to the value of the correlation coefficient Glxs;

[0058] The security sharing module is used to perform a combination analysis on the sensitive parameters based on the preliminary screening of sensitive parameters, and after the combination, analyze the distribution changes of each combination pair to construct the discrete degree coefficient of each combination pair. , and pre-set the safety range , by dividing the dispersion coefficient of each combination pair by and safety range Conduct comparative analysis to determine whether compensatory measures are necessary and, if necessary, issue a correction order;

[0059] The compensation module is used to, after receiving the compensation command, calculate the discrete degree coefficient of the corresponding combination pair. , construct the compensation index Bczs, and combine it with the safety range , introducing noise compensation.

[0060] During the operation of this system, by introducing the correlation coefficient Glxs analysis and combination analysis, the system can accurately calculate the correlation between different parameters, thereby identifying potential sensitive data combinations, and further combining the discrete degree coefficient of each combination pair , can effectively assess the privacy risk after data combination, and take compensatory measures when necessary, such as introducing noise compensation, to ensure that shared data is within a safe range, protect sensitive information as much as possible, and avoid privacy leakage. The use of blockchain technology to trace and verify each parameter sequence set ensures the transparency and credibility of the data source. This mechanism not only improves the reliability of the data, but also prevents the tampering or loss of data during transmission and sharing, laying a solid foundation for the safe sharing of marine big data. By constructing the discrete degree coefficient and compensation index Bczs. The system can dynamically adjust the data sharing strategy according to the real-time status and analysis results of the data. After identifying potential privacy risks, the system can automatically issue correction commands and make compensation, ensuring that the data is always in a controlled and secure state during the sharing process, thereby improving the flexibility and intelligence of data sharing.

[0061] Example 2

[0062] Please refer to Figure 1 Specifically: the data collection module is used to monitor and collect ocean data sets in real time from multiple groups of ocean sensors, ocean observation satellites and ship-borne meteorological stations, wherein the ocean data sets include relevant hydrological data, relevant meteorological data and relevant biological data in the ocean.

[0063] The data processing module includes a cleaning unit, a classification unit and a data tracing unit;

[0064] The cleaning unit is used to identify and remove duplicate values ​​and abnormal values ​​in the ocean data set, and perform feature extraction on the ocean data set to reduce data dimensions and remove redundant information;

[0065] The classification unit is used to classify the relevant hydrological data, relevant meteorological data and relevant biological data in the ocean data set, extract the parameters in different monitoring periods, and establish each parameter sequence set respectively;

[0066] The data tracing unit is used to generate a unique hash value for each parameter in each parameter sequence set using blockchain technology for data tracing and verification, ensuring the integrity and non-tamperability of the data, and automatically recording the visitor's operation record each time the data is accessed or shared, wherein the operation record content includes the data identifier, operation type, timestamp and operation user, so as to form a detailed access record.

[0067] In this embodiment, the data collection module monitors and collects marine data sets in real time through a variety of devices. This multi-source data collection method ensures the breadth and timeliness of marine data, enables the system to comprehensively cover different aspects of the marine environment, and provides a highly accurate and rich data foundation. The cleaning unit in the data processing module effectively identifies and removes duplicate values ​​and outliers in the data set, and performs feature extraction at the same time. This process not only improves the quality of the data and reduces redundant information, but also reduces the complexity of the data through dimensionality reduction processing, and optimizes the efficiency of subsequent analysis. The classification unit performs detailed parameter classification on the hydrological data, meteorological data, and biological data in the marine data set, and establishes a sequence set of each parameter. This refined classification and management method ensures the systematic and structured nature of the data in different monitoring periods, provides a clear reference basis for subsequent correlation analysis and security assessment, and improves the operability of the data. The data traceability unit generates a unique hash value for each parameter through blockchain technology, and records detailed operation records each time the data is accessed or shared. This design ensures the integrity and immutability of the data and provides a strong security guarantee. The detailed traceability function of operation records not only ensures the transparency of data in the sharing process, but also provides a tracking basis for the legality and security of data access, effectively preventing data leakage and abuse. Overall, the system design further improves the management and sharing efficiency of marine big data through multi-dimensional data collection, efficient processing, accurate classification and reliable traceability, ensuring the security and integrity of data throughout the life cycle, especially in the process of multi-source data sharing and dynamic tracing, effectively reducing potential privacy risks.

[0068] Example 3

[0069] Please refer to Figure 1 ,Specifically: the data preliminary analysis module includes a mutual impact analysis unit and a screening unit;

[0070] The mutual influence analysis unit is used to perform correlation analysis on each parameter sequence set after classification to construct the correlation coefficient Glxs between the corresponding parameters, which is specifically obtained in the following manner:

[0071] ;

[0072] In the formula, X and Y are two random parameters in the set of all parameter sequences, representing two different data parameters analyzed in the ocean big data; It is the joint probability distribution of parameter 1 X and parameter 2 Y, which indicates the probability that parameter 1 X and parameter 2 Y take a specific value at the same time during the same monitoring period; It is the marginal probability distribution of parameter X, which indicates the probability when parameter X has the value x. It is the marginal probability distribution of the second parameter Y, which indicates the probability that the value of the second parameter Y is y.

[0073] Indicates a specific value of parameter X. For example, if parameter X represents temperature, then x can be a specific temperature value such as 25°C or 26°C. It represents a specific value of the second parameter Y. For example, if the second parameter Y represents salinity, then y can be a specific salinity value such as 35‰ or 36‰.

[0074] Analyze the correlation between different data to identify data that may not be sensitive alone but may lead to sensitive information leakage when combined with other data. For example, ocean temperature data alone may not be sensitive, but when combined with ship activity data, it may infer certain sensitive route information.

[0075] The screening unit is used to calculate the correlation coefficients Glxs between several groups of parameters according to the method of obtaining the correlation coefficients Glxs between the corresponding parameters in the mutual influence analysis unit, and obtain the average correlation coefficients between the parameters through a statistical algorithm. and the standard deviation of the correlation coefficient between parameters , based on the average correlation coefficient between parameters and the standard deviation of the correlation coefficient between parameters , set the threshold G, which is set in the following way: ; k is a constant, usually with a value of 1-3, corresponding to different confidence levels. The specific value is set by the user (based on actual conditions);

[0076] By comparing the correlation coefficient Glxs between the corresponding parameters with the threshold G, sensitive parameters can be preliminarily screened out. The specific screening contents are as follows:

[0077] If the correlation coefficient Glxs between the corresponding parameters exceeds the threshold G, it means that there is a correlation risk between the corresponding parameters. At this time, the sensitive parameters will be preliminarily marked and screened out;

[0078] If the correlation coefficient Glxs between the corresponding parameters does not exceed the threshold G, it means that there is no correlation risk between the corresponding parameters, and no marking and screening will be performed at this time.

[0079] In this embodiment, through the mutual influence analysis unit in the preliminary analysis module between data, the system can effectively perform correlation analysis on each parameter sequence set after classification, and construct the correlation coefficient Glxs between the corresponding parameters. This analysis helps to identify the potential correlation between each parameter in the marine big data, especially when analyzing the marine environmental changes and their influencing factors, providing strong data support. The screening unit calculates the correlation coefficient Glxs between several groups of parameters, and uses a statistical algorithm to obtain the average correlation coefficient and standard deviation between the parameters, further sets the threshold G, and uses this as a benchmark to screen the parameters. This scientific screening process ensures that the system can effectively identify potential sensitive parameters when processing marine big data, further avoiding misjudgment or missed judgment caused by insufficient correlation between parameters, thereby improving the accuracy and reliability of data analysis. By comparing the calculated correlation coefficient Glxs with the preset threshold G, the system can automatically identify and mark sensitive parameters with associated risks. This automated risk identification function not only improves the work efficiency of the system, but also ensures the real-time and timeliness of data security management, thereby effectively preventing possible privacy leaks or data security risks. The screening unit screens parameters through the set threshold G, where k is represented as a constant. This flexible dynamic threshold setting enables the system to adjust the threshold according to different analysis requirements or data environments, thereby improving the adaptability and accuracy of the system in different scenarios and meeting the diverse data analysis and security management needs. Through the refined correlation analysis and sensitive parameter screening mechanism in the system, the system can warn and mark parameters that may have data association risks in advance, thereby providing an effective basis for subsequent security sharing and compensation. This automatic screening and marking function based on the correlation coefficient not only improves the overall security of the marine big data security sharing system, but also effectively prevents privacy leakage caused by improper correlation analysis, and significantly enhances the effect of data security and privacy protection. In summary, the system effectively improves the security and reliability of marine big data in the sharing process through precise parameter correlation analysis, scientific sensitive parameter screening mechanism and flexible threshold setting, and provides strong technical support for the security management and privacy protection of marine data.

[0080] Example 4

[0081] Please refer to Figure 1 ,Specifically: the security sharing module includes a combination unit, a combination analysis unit and an identification unit;

[0082] The combination unit is used to perform statistics on the sensitive parameters screened in the screening unit, and combine several groups of sensitive parameters in combination with probability theory to obtain several groups of combination pairs.

[0083] The combination analysis unit is used to analyze the distribution changes of each combination pair to construct the dispersion coefficient of each combination pair. , which can be obtained by:

[0084] ;

[0085] In the formula, Z is a sensitive parameter. is the probability density of uncombined sensitive information, which represents the probability distribution of a single dimension (such as temperature) in its original, uncombined state; It is the probability density of the combined sensitive information, which represents the joint probability distribution of multiple dimensions, that is, the probability of the combination of different features appearing in the entire data set; It represents a directionality, i.e., the divergence of the uncombined sensitive information relative to the combined sensitive information; Indicates that z is taken from a value in the value space of the sensitive parameter Z, and is used to traverse the entire value space for summation.

[0086] The identification unit is used to identify the discrete degree coefficient of each combination pair Through statistical algorithms, the average dispersion coefficient of the combination pairs is obtained respectively. and the standard deviation of the coefficient of dispersion of the combination , by combining the average dispersion coefficient of the pair and the standard deviation of the coefficient of dispersion of the combination , set the safety range , and set it as follows:

[0087] ; ;

[0088] Where A is a constant;

[0089] By using the dispersion coefficient of each combination pair and safety range Comparative analysis is conducted to determine whether compensation measures need to be introduced. The specific determination is as follows:

[0090] If the dispersion coefficient of the corresponding combination pair Falling into the safe range When the amount is within the limit, it is determined that no compensatory measures are necessary;

[0091] If the dispersion coefficient of the corresponding combination pair Not within the safety range If it is determined that compensatory measures are necessary, a correction order will be issued externally.

[0092] In this embodiment, through the combination unit and the combination analysis unit in the security sharing module, the system can combine the sensitive parameters preliminarily screened in multiple dimensions, and use the discrete degree coefficient after combination. , accurately evaluate the distribution changes of sensitive information under different combinations. This process combines probability theory and statistical algorithms, allowing the system to deeply understand the potential correlation and risks between data in each dimension. This multi-dimensional analysis provides a reliable basis for subsequent data sharing and processing, and effectively avoids the risk of misjudgment and data leakage that may be caused by single-dimensional analysis. The recognition unit sets a reasonable safety range by calculating the average dispersion coefficient and standard deviation of each combination pair. . By calculating the dispersion coefficient of each combination pair With the set safety range By comparing and analyzing, the system can intelligently identify whether there are potential risks and decide whether to introduce compensatory measures. Beyond safety range When the system automatically issues a correction command to introduce compensation measures to further ensure the security of data during sharing, this mechanism not only improves the system's security protection capabilities for sensitive information, but also enhances the reliability of data sharing. The system can dynamically adjust the introduction of compensation measures based on the discrete degree coefficient of the combination pair and the set security range. This mechanism ensures that the system can respond quickly, identify high-risk combinations in time, and take necessary compensation measures when facing massive and changeable marine big data. This dynamic adjustment capability ensures the efficient operation of the system and improves the security and stability of the data sharing process. Through fine combination analysis and calculation of the discrete degree coefficient, the system can deeply mine possible sensitive information combinations during data sharing, and prevent data leakage and privacy infringement through reasonable compensation measures. This all-round protection measure not only improves the sharing efficiency of marine big data, but also ensures data security and privacy protection, and fully protects the data rights and interests of users. In summary, the system further improves the security and privacy protection capabilities of marine big data sharing through accurate sensitive information combination analysis, intelligent risk identification and compensation mechanism, and dynamic adjustment capabilities. When faced with complex marine environmental data, the system can effectively identify, analyze and handle potential risks, thereby ensuring the security and efficient sharing of data.

[0093] Example 5

[0094] Please refer to Figure 1 ,Specifically: the compensation module includes a compensation analysis unit and a correction unit;

[0095] The compensation analysis unit is used to introduce noise into the sensitive parameters involved in the corresponding combination pair after receiving the compensation command, and combine the discrete degree coefficient of the corresponding combination pair , construct the corresponding compensation index Bczs, which can be obtained in the following way:

[0096] ;

[0097] In the formula, Indicates the discrete degree coefficient of each input combination pair When any record in changes, the maximum value of the change is output; c is represented as the demand constant, which is used to quantify the strength of privacy protection. The smaller the value, the stronger the privacy protection and the greater the added noise; the larger the value, the weaker the privacy protection and the smaller the added noise;

[0098] Here It can be understood as a correction term, which introduces noise to adjust the discrete degree coefficient compensation; Lap represents the Laplace distribution, which is a symmetric probability distribution used to generate noise;

[0099] The correction unit is used to compensate and analyze the sensitive parameters after the noise is introduced, combined with the safety range , by comparing the corresponding compensation index Bczs again to see if it falls within the safe range To determine whether it is necessary to continue the noise introduction operation, if the corresponding compensation index Bczs falls within the safe range , then stop the noise introduction operation.

[0100] In this embodiment, the compensation analysis unit can effectively improve the privacy protection level of the data by introducing noise into the sensitive parameters and constructing the compensation index Bczs in combination with the discrete degree coefficient. Here, the constant c is used to quantify the strength of privacy protection. The smaller the value, the stronger the privacy protection and the greater the noise added, thereby enhancing the confidentiality of the data. The introduced noise further enhances the protection of sensitive information and ensures that user privacy will not be leaked during data sharing. The correction unit can dynamically adjust the degree of noise introduction by performing compensation analysis on the sensitive parameters after the noise is introduced and combining the safety range. By comparing and analyzing the compensation index Bczs, the system can determine in real time whether it is necessary to continue to introduce noise. If the compensation index Bczs falls into the safety range, the noise introduction operation is stopped. This dynamic adjustment mechanism ensures the rationality of noise introduction, further avoids excessive or insufficient privacy protection, and improves the flexibility and efficiency of data processing. The sensitive parameters after the noise is introduced can effectively cover up the sensitive information in the data and reduce the risk of data leakage. This process ensures that sensitive information will not be maliciously acquired or abused even in the case of data sharing. The design of the compensation module ensures that data sharing activities can proceed smoothly while meeting privacy protection requirements, while protecting users' sensitive information from being leaked. While protecting data privacy, the compensation module of the system optimizes the privacy protection strategy through the cooperation of compensation analysis and correction units. By accurately controlling the amount of noise introduced and adjusting the privacy protection intensity, the system can balance the security and effectiveness of data sharing and improve the protection level in the data sharing process. In summary, the compensation module can effectively improve the level of data privacy protection by introducing noise and dynamically adjusting privacy protection measures, ensure security in the data sharing process, and optimize the privacy protection strategy, providing a reliable solution for the secure sharing of marine big data.

[0101] Example 6

[0102] Please refer to Figure 1 and Figure 2 ,Specifically: A method for safely sharing ocean big data based on dynamic data tracing, comprising the following steps,

[0103] Step 1: First, use multiple sets of monitoring instruments to monitor the ocean state in real time to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set;

[0104] Step 2: Filter and correct the collected raw data, remove outliers, extract key data, and classify and count the extracted data to obtain each parameter sequence set. At the same time, use blockchain technology to trace and verify each parameter sequence set.

[0105] Step 3: Then, the classified parameter sequence sets are subjected to correlation analysis to construct the correlation coefficient Glxs, and sensitive information is preliminarily screened according to the value of the correlation coefficient Glxs;

[0106] Step 4: After the sensitive information is initially screened out, the sensitive information is combined and analyzed. After the combination, the distribution changes of each combination pair are analyzed to construct the discrete degree coefficient Lcxs of each combination pair, and the security threshold G is pre-set. By comparing and analyzing the discrete degree coefficient Lcxs of each combination pair with the security threshold G, it is determined whether compensation measures need to be introduced. If necessary, a correction command is issued;

[0107] Step 5: Finally, after receiving the compensation command, the compensation index Bczs is constructed according to the discrete degree coefficient Lcxs of the corresponding combination pair, and noise compensation is introduced in combination with the safety threshold G.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A marine big data security sharing system based on dynamic data tracing, characterized by: It includes data collection module, data processing module, data preliminary analysis module, security sharing module and compensation module; The data collection module is used to monitor the ocean state in real time using multiple sets of monitoring instruments to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set; The data processing module is used to filter and correct the collected raw data, remove outliers, and extract key data, and classify and count the extracted data to obtain various parameter sequence sets, and at the same time use blockchain technology to trace and verify each parameter sequence set; The data preliminary analysis module is used to perform correlation analysis on each parameter sequence set after classification to construct a correlation coefficient Glxs, and preliminarily screen sensitive parameters according to the value of the correlation coefficient Glxs; The data preliminary analysis module includes a mutual impact analysis unit and a screening unit; The mutual influence analysis unit is used to perform correlation analysis on each parameter sequence set after classification to construct the correlation coefficient Glxs between the corresponding parameters, which is specifically obtained in the following manner: ； In the formula, X and Y are two random parameters in the set of all parameter sequences; It is the joint probability distribution of parameter 1 X and parameter 2 Y; is the marginal probability distribution of parameter X; is the marginal probability distribution of parameter 2 Y; The security sharing module is used to perform a combination analysis on the sensitive parameters based on the preliminary screening of sensitive parameters, and after the combination, analyze the distribution changes of each combination pair to construct the discrete degree coefficient of each combination pair. , and pre-set the safety range , by dividing the dispersion coefficient of each combination pair by and safety range Conduct comparative analysis to determine whether compensatory measures are necessary and, if necessary, issue a correction order; The security sharing module includes a combination unit, a combination analysis unit and an identification unit; The combination analysis unit is used to analyze the distribution changes of each combination pair to construct the dispersion coefficient of each combination pair. , which can be obtained by: ； In the formula, Z is a sensitive parameter. is the probability density of uncombined sensitive information; is the probability density of the combined sensitive information; It represents a directionality, i.e., the divergence of the uncombined sensitive information relative to the combined sensitive information; The compensation module is used to, after receiving the compensation command, calculate the discrete degree coefficient of the corresponding combination pair. , construct the compensation index Bczs, and combine it with the safety range , introduce noise compensation; The compensation module includes a compensation analysis unit and a correction unit; The compensation analysis unit is used to introduce noise into the sensitive parameters involved in the corresponding combination pair after receiving the compensation command, and combine the discrete degree coefficient of the corresponding combination pair , construct the corresponding compensation index Bczs, which can be obtained in the following way: ； In the formula, Indicates the discrete degree coefficient of each input combination pair When any record in changes, the maximum value of the output change; c is represented by the demand constant; The correction unit is used to compensate and analyze the sensitive parameters after the noise is introduced, combined with the safety range , by comparing the corresponding compensation index Bczs again to see if it falls within the safe range To determine whether it is necessary to continue the noise introduction operation, if the corresponding compensation index Bczs falls within the safe range , then stop the noise introduction operation.

2. According to claim 1, a marine big data security sharing system based on dynamic data tracing is characterized by: The data collection module is used to monitor and collect ocean data sets from multiple groups of ocean sensors, ocean observation satellites and ship-borne weather stations in real time, wherein the ocean data sets include relevant hydrological data, relevant meteorological data and relevant biological data in the ocean.

3. The marine big data security sharing system based on dynamic data tracing according to claim 1 is characterized by: The data processing module includes a cleaning unit, a classification unit and a data tracing unit; The cleaning unit is used to identify and remove duplicate values ​​and abnormal values ​​in the ocean data set, and perform feature extraction on the ocean data set to reduce data dimensions and remove redundant information; The classification unit is used to classify the relevant hydrological data, relevant meteorological data and relevant biological data in the ocean data set, extract the parameters in different monitoring periods, and establish each parameter sequence set respectively; The data tracing unit is used to generate a unique hash value for each parameter in each parameter sequence set using blockchain technology, and automatically record the visitor's operation record each time the data is accessed or shared, wherein the operation record content includes the data identifier, operation type, timestamp and operation user.

4. The marine big data security sharing system based on dynamic data tracing according to claim 1 is characterized by: The screening unit is used to calculate the correlation coefficients Glxs between several groups of parameters according to the method of obtaining the correlation coefficients Glxs between the corresponding parameters in the mutual influence analysis unit, and obtain the average correlation coefficients between the parameters through a statistical algorithm. and the standard deviation of the correlation coefficient between parameters , based on the average correlation coefficient between parameters and the standard deviation of the correlation coefficient between parameters , set the threshold G, which is set in the following way: ; k is represented as a constant; By comparing the correlation coefficient Glxs between the corresponding parameters with the threshold G, sensitive parameters can be preliminarily screened out. The specific screening contents are as follows: If the correlation coefficient Glxs between the corresponding parameters exceeds the threshold G, it means that there is a correlation risk between the corresponding parameters. At this time, the sensitive parameters will be preliminarily marked and screened out; If the correlation coefficient Glxs between the corresponding parameters does not exceed the threshold G, it means that there is no correlation risk between the corresponding parameters, and no marking and screening will be performed at this time.

5. The marine big data security sharing system based on dynamic data tracing according to claim 1 is characterized by: The combination unit is used to perform statistics on the sensitive parameters screened in the screening unit, and combine several groups of sensitive parameters in combination with probability theory to obtain several groups of combination pairs.

6. The marine big data security sharing system based on dynamic data tracing according to claim 1 is characterized by: The identification unit is used to identify the discrete degree coefficient of each combination pair Through statistical algorithms, the average dispersion coefficient of the combination pairs is obtained respectively. and the standard deviation of the coefficient of dispersion of the combination , by combining the average dispersion coefficient of the pair and the standard deviation of the coefficient of dispersion of the combination , set the safety range , and set it as follows: ； ； Where A is a constant; By using the dispersion coefficient of each combination pair and safety range Comparative analysis is conducted to determine whether compensation measures need to be introduced. The specific determination is as follows: If the dispersion coefficient of the corresponding combination pair Falling into the safe range When the amount is within the limit, it is determined that no compensatory measures are necessary; If the dispersion coefficient of the corresponding combination pair Not within the safety range If it is determined that compensatory measures are necessary, a correction order will be issued externally.

7. A method for safely sharing ocean big data based on dynamic data tracing, used to implement a system for safely sharing ocean big data based on dynamic data tracing as described in any one of claims 1 to 6, characterized in that: The following steps are included: Step 1: First, use multiple sets of monitoring instruments to monitor the ocean state in real time to obtain relevant hydrological data, relevant meteorological data and relevant biological data, and generate an ocean data set; Step 2: Filter and correct the collected raw data, remove outliers, extract key data, and classify and count the extracted data to obtain each parameter sequence set. At the same time, use blockchain technology to trace and verify each parameter sequence set. Step 3: Then, the classified parameter sequence sets are subjected to correlation analysis to construct the correlation coefficient Glxs, and sensitive information is preliminarily screened according to the value of the correlation coefficient Glxs; Step 4: After the sensitive information is initially screened out, the sensitive information is combined and analyzed. After the combination, the distribution changes of each combination pair are analyzed to construct the discrete degree coefficient Lcxs of each combination pair, and the security threshold G is pre-set. By comparing and analyzing the discrete degree coefficient Lcxs of each combination pair with the security threshold G, it is determined whether compensation measures need to be introduced. If necessary, a correction command is issued; Step 5: Finally, after receiving the compensation command, the compensation index Bczs is constructed according to the discrete degree coefficient Lcxs of the corresponding combination pair, and noise compensation is introduced in combination with the safety threshold G.

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