Processing method and system for dynamic encryption of enterprise sensitive data

Abstract

The invention discloses a processing method and system for dynamic encryption of enterprise sensitive data, and particularly relates to the field of secret communication and data security, and the method comprises the steps: building a key management system which is responsible for generating and distributing an encryption key, and enabling enterprise equipment to be registered to the system through a channel and to obtain the encryption key when the enterprise equipment is used for the first time; an operating system or a hardware level in the equipment regularly checks the state of the system, establishes an environment evaluation level of the equipment according to the sensitivity of the data and an operating environment, adjusts an encryption level in real time according to the environment evaluation level, and dynamically encrypts the data at the same time; when the equipment is scrapped or faced with a security risk, an administrator can remotely make the secret key invalid and evaluate the clearance rate of data left by the equipment; by dynamically encrypting and evaluating the data in the waste equipment of the enterprise, the clearance rate evaluation index is generated, and the key problem that the data clearance effect cannot be quantified in the traditional deletion operation is solved.

Description

Technical Field

[0001] The present invention relates to the field of confidential communication and data security technology, and more specifically, to a processing method and system for dynamic encryption of enterprise sensitive data. Background Art

[0002] Sensitive data refers to key information that should not be disclosed or shared without authorization, usually including personal information, business secrets, customer data, financial information, product design, intellectual property, and government confidential information. With the development of digitalization, the storage and transmission of sensitive data mostly rely on electronic devices and network systems. Once leaked, this data may lead to privacy infringement, economic losses, legal disputes, or reduced corporate competitiveness. Existing technologies usually protect the security of sensitive data through encryption, access control, and data classification to prevent unauthorized access and use.

[0003] When an enterprise's old equipment is scrapped or a security risk occurs, the safe handling of sensitive data is an important issue. Traditional solutions often only perform simple deletion operations, but a key problem with this approach is that it is impossible to accurately measure the data clearance rate, especially when the data sensitivity is high, and it is impossible to quantify the clearance effect of the remaining data. Therefore, the data clearance rate becomes an unknown key indicator, and the enterprise cannot accurately judge the remaining data risks when handling old equipment; this not only affects the security of data, but also leads to the risk of data leakage. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a processing method and system for dynamically encrypting enterprise sensitive data. By dynamically encrypting and evaluating the data in the enterprise's waste equipment, it is ensured that the legacy data can be effectively cleared when the equipment is scrapped or there is a security risk, and a clearance rate evaluation index is generated to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a processing method for dynamic encryption of enterprise sensitive data, comprising:

[0006] Establish a key management system, which is responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time.

[0007] The operating system or hardware layer within the device regularly checks the system status, establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment, adjusts the encryption level in real time based on the environmental assessment level, and dynamically encrypts the data;

[0008] When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and evaluate the device's remaining data removal rate;

[0009] The key management system is used to obtain data residue information, key recovery efficiency information, and data coverage depth information, and a comprehensive evaluation index for the clearance rate is generated for rating processing.

[0010] In a preferred embodiment, the proposed device collects operation-related state information at each time point t, and the state information includes operating environment information and data sensitivity information; the state information is represented by a multidimensional vector Q(t), and Q(t) contains n different state variables, namely:

[0011] Q(t)={q1(t),q2(t),…,q n (t)}

[0012] Among them, q i (t) represents the value of the i-th state at time t. By analyzing the state information, a multi-weight distribution model is used to extract the features of the state information. The model reflects the changes of the state under different weights through a multi-component distribution function, and its expression is:

[0013]

[0014] In the formula p(Q(t)), p represents the probability density function, which is used to represent the probability distribution of the state information Q(t) at time t, η j is the weight coefficient of the jth state component, represents the probability distribution of the jth component, μ j is the center point of the state component, Δ j is the fluctuation amplitude of the state component;

[0015] Based on the currently collected state information Q(t), the risk assessment of the environment in which the equipment is located is carried out, and the environmental assessment function is proposed to be G(t) to represent the risk level of the equipment at time t. The contribution of each state variable is expressed as a product in the form of a nonlinear combination as an overall assessment value. The calculation formula of G(t) is:

[0016]

[0017] Among them, γ k is the influence coefficient of the kth state feature, indicating the weight of this feature in environmental assessment; Φ k (q k (t)) is the state feature q k (t) mapped to a nonlinear function of risk factors;

[0018] Based on the device environment evaluation function G(t), an encryption strategy is selected to deal with different risk levels; the encryption strategy is defined as P(t), which represents the encryption scheme adopted by the device at time t; the encryption strategy is selected by maximizing the cumulative benefit, hoping to obtain the maximum security benefit at both the current moment and the future moment; the specific decision formula is:

[0019]

[0020] Among them, B is the set of all possible encryption strategies, is the instantaneous profit function of the encryption strategy p at the current moment when the environment is evaluated as G(t), which indicates the adaptability of the encryption strategy to the current risk; ζ is the discount factor, which determines the discount rate of future returns. is the long-term return function at the next time point.

[0021] In a preferred embodiment, an encryption strength J(t) is proposed, and the encryption strength J(t) represents the encryption level adopted by the device at time t. The encryption level is adjusted according to the current state of the device and the expected requirements. The encryption strength is optimized by minimizing the error of the encryption strength adjustment amount through the Lagrangian optimization model:

[0022]

[0023] Among them, δ represents the adjustment amount of encryption strength, J optimal (t) is the expected encryption strength, which represents the ideal encryption strength at the current time t; is the square term of the rate of change of encryption strength, and λ is the regularization parameter that controls the rate of change of encryption strength;

[0024] A destruction mechanism based on dynamic probability is introduced. The trigger condition of data destruction is calculated through a dynamic probability function to determine whether the device should destroy the data. The destruction trigger probability ρ(t) is defined as:

[0025]

[0026] Among them, α is the growth coefficient, which controls the rate of change of the trigger probability, G threshold is the risk threshold of the destruction mechanism; when the risk assessment value G(t) of the device exceeds this threshold, the trigger probability ρ(t) of data destruction increases, and sensitive data can be destroyed in a high-risk environment;

[0027] At each time point t, the encryption strategy and strength are optimized based on historical data, and the cumulative utility function U(t) is introduced. Function U(t) integrates the encryption strength J(t) and encryption strategy P(t), and adjusts the current encryption scheme based on historical risk assessment results:

[0028]

[0029] Among them, ρ m is the destruction probability at the mth moment, J(m) is the encryption strength at that moment, and P(m) is the encryption strategy at that moment. The optimization goal is to maximize the cumulative utility U(t).

[0030] In a preferred embodiment, a data residue index D is generated based on the data residue information. res ,The data residual index is used to evaluate the data clearing situation in the device; the proposed device contains N data blocks, d i represents the residual state of the i-th data block. If d i =1 means the data block has not been cleared. i = 0 means it is completely cleared; the residual effect of clearing is captured by introducing a formula with nonlinear adjustment:

[0031]

[0032] Where N is the total number of data blocks in the device, indicating how many data blocks need to be processed; d i is the residual state of the i-th data block; ξ i is the location complexity, which indicates the complexity of the physical or logical storage location of the i-th data block; s i is the sensitivity coefficient of data block i; α i is a nonlinear adjustment parameter used to control s i The intensity of the residual effect; θ i represents the residual inflection point threshold of the data block; e is a natural constant;

[0033] Generate a key recovery efficiency index K based on key recovery efficiency information eff ,The key recovery efficiency index is used to evaluate the recovery efficiency of the ,device’s encryption key after it is remotely invalidated by the ,administrator, and simulates the key invalidation and recovery process through time-dependent ,nonlinear feedback;

[0034]

[0035] Where k(t) is the key state function. When the key is valid at time t, k(t) = 1; when the key is invalid, k(t) = 0. response The response time for key recovery to start; It represents the complexity of the system response, which affects the difficulty of key recovery. η and β are parameters that control the key recovery rate. is a decay function that describes the rate of change of key recovery over time; γ is the influence of the effective state of the control key on the time-dependent feedback function, and log(1+k(t)·γ·t) introduces the time dynamic response of the key validity; Tmax is the maximum time window for monitoring key recovery operations;

[0036] Generate a data coverage depth index O based on the data coverage depth information depth ,The data coverage depth index is used to evaluate the coverage depth of the device when performing data erasure or overwriting operations. The depth index of the overwriting operation is calculated by introducing the number of overwriting times and the risk attenuation function:

[0037]

[0038] where ζ i (t) represents the risk attenuation coefficient of the i-th data block at time t; z i (t) is the number of times the i-th data block is covered at time t; λ i Used to adjust the impact of the coverage effect; κ i Indicates the threshold for the overwrite operation.

[0039] In a preferred embodiment, based on the data residual index D res , key recovery efficiency index K eff and data coverage depth index O depth Generate a comprehensive assessment index of clearance rate C clear ; The final comprehensive evaluation index of clearance rate is generated through nonlinear combination:

[0040]

[0041] Among them, δ1, δ2 and β3 are weight parameters for controlling the influence of each index on the comprehensive evaluation index of clearance rate; α1 is the residual index D of the control data res The rate of change of; μ1 is the threshold value of the data residual index; represents the square of the key recovery efficiency; Represents the logarithmic function of coverage depth.

[0042] In a preferred embodiment, the system generates a dynamic encryption key based on the state information Q(t) and the risk assessment result G(t). The key generation process is based on a nonlinear function of the device state, and the key is sliced ​​by different state parameters.

[0043]

[0044] Where K(t) is the dynamic key, which is the encryption key generated at time t according to the state parameters; k i (t) is the slice part of the key; α i and θ i To control the nonlinear changes of key slices;

[0045] The encryption strategy is dynamically selected based on the risk assessment result G(t). Each layer of encryption strategy selects the encryption algorithm adaptively and dynamically nests the encryption formula:

[0046] C dyn (M) = ε outer (ε inner (M,K inner (t)),K outer (t))

[0047] Where M is the data packet that needs to be encrypted; ε inner and ε outer They are the inner encryption algorithm and the outer encryption algorithm respectively; K inner (t) and K outer (t) are the inner encryption key and the outer encryption key respectively;

[0048] The levels of risk assessment results include low risk level, medium risk level and high risk level; two risk level thresholds are proposed: threshold τ1 and threshold τ2;

[0049] Low risk level: When the risk level G(t) of the device is lower than the preset threshold τ1, the system selects the basic encryption method, which includes the symmetric encryption algorithm;

[0050] Medium risk level: When the risk level G(t) is between the two preset thresholds τ1 and τ2, the intermediate encryption method is used; the inner layer uses AES-256 symmetric encryption, and the outer layer uses RSA-2048 asymmetric encryption for additional protection;

[0051] High risk level: When the risk level G(t) exceeds the threshold τ2, an advanced encryption strategy is adopted. At this time, the inner layer performs multiple chain encryptions, and the outer layer uses RSA-4096 encryption, combined with multi-signature technology to ensure that data is protected in a high-risk environment;

[0052] According to the transmitted data state vector Q(t) and the key model stored in the key management system, the decryption path is generated:

[0053]

[0054] in and They are inner decryption function and outer decryption function respectively;

[0055] The system adopts a dynamic key recovery mechanism and a multi-layer data destruction strategy;

[0056] Key recovery mechanism: The key management system monitors the risk assessment value G(t) and the key recovery efficiency index K eff , determine whether the key needs to be recycled or invalidated;

[0057] Data destruction mechanism: If the device detects a physical attack or high-risk status, the system will destroy sensitive data through a data overwrite mechanism. The specific data destruction formula is as follows:

[0058]

[0059] Among them, clear (M) represents the coverage depth index of the clearing operation.

[0060] In a preferred embodiment, a processing system for dynamic encryption of sensitive enterprise data includes a key management system module, a device status monitoring and evaluation module, an encryption dynamic adjustment module, a remote key expiration and data evaluation module, and a data purge rate evaluation and rating module;

[0061] Key management system module: responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time.

[0062] Equipment status monitoring and assessment module: The operating system or hardware layer within the device regularly checks the system status and establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment;

[0063] Encryption dynamic adjustment module: adjusts the encryption level in real time according to the environmental assessment level, and performs dynamic encryption processing on the data;

[0064] Remote key expiration and data assessment module: When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and assess the clearance rate of the device's remaining data;

[0065] Data clearance rate assessment and rating module: obtains data residue information, key recovery efficiency information, and data coverage depth information through the key management system, and generates a comprehensive clearance rate assessment index for rating processing.

[0066] Technical effects and advantages of the present invention:

[0067] 1. This solution dynamically encrypts and evaluates the data in the enterprise's obsolete equipment to ensure that the remaining data can be effectively cleared when the equipment is scrapped or there is a safety risk, and generates a clearing rate evaluation index, which solves the key problem that the data clearing effect cannot be quantified in traditional deletion operations;

[0068] 2. According to the operating environment and data sensitivity of the device, the encryption strategy is adjusted in real time to match the encryption level, ensuring the data security of the device under different risk conditions and improving the flexibility of encryption processing;

[0069] 3. When a device is scrapped or a security risk occurs, the administrator can use remote commands to immediately invalidate the device's encryption key to prevent sensitive data leakage and achieve full security management of the device life cycle;

[0070] 4. This solution can dynamically encrypt data based on the current status and environment of the device, adapt to changes in risk, and automatically select the appropriate encryption method to ensure data security in low, medium, and high risk environments;

[0071] 5. This solution can not only evaluate data residue, but also combine key recovery efficiency and data coverage depth to generate a comprehensive removal rate, comprehensively evaluate and ensure the thoroughness of data removal. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0073] 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.

[0074] Refer to the instruction manual Figure 1 The present invention relates to the field of confidential communication and data security, which can be understood as dynamic encryption and key management. A processing method for dynamic encryption of sensitive enterprise data in one embodiment of the present invention includes:

[0075] Establish a key management system, which is responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time.

[0076] The operating system or hardware layer within the device regularly checks the system status, establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment, adjusts the encryption level in real time based on the environmental assessment level, and dynamically encrypts the data;

[0077] When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and evaluate the device's remaining data removal rate;

[0078] The key management system is used to obtain data residue information, key recovery efficiency information, and data coverage depth information, and a comprehensive evaluation index for the clearance rate is generated for rating processing.

[0079] Furthermore, it is proposed that the device collects operation-related status information at each time point t. The status information includes operating environment information and data sensitivity information. The operating environment information includes device temperature, network connection status, physical location, etc., which is used to reflect the current external conditions of the device; data sensitivity information includes data importance, access frequency, data type, and potential risks, etc.; the status information is represented by a multidimensional vector Q(t), which contains n different state variables, namely:

[0080] Q(t)={q1(t),q2(t),…,q n (t)}

[0081] Among them, q i (t) represents the value of the i-th state at time t. By analyzing the state information, a multi-weight distribution model is used to extract the features of the state information. The model reflects the changes of the state under different weights through a multi-component distribution function, and its expression is:

[0082]

[0083] In the formula p(Q(t)), p represents the probability density function, which is used to represent the probability distribution of the state information Q(t) at time t, η j is the weight coefficient of the jth state component, represents the probability distribution of the jth component, μ j is the center point of the state component, Δ j is the fluctuation amplitude of the state component; through this process, the system extracts the features that best reflect the current state of the equipment, providing a basis for subsequent risk assessment;

[0084] Based on the currently collected state information Q(t), the risk assessment of the environment in which the equipment is located is carried out, and the environmental assessment function is proposed to be G(t) to represent the risk level of the equipment at time t. The contribution of each state variable is expressed as a product in the form of a nonlinear combination as an overall assessment value. The calculation formula of G(t) is:

[0085]

[0086] Among them, γ k is the influence coefficient of the kth state feature, indicating the weight of this feature in environmental assessment; Φ k (q k (t)) is the state feature q k (t) is mapped into a nonlinear function of the risk factor, such as an exponential function or a logarithmic function. In this way, the system can capture the nonlinear changes of the state characteristics and more accurately assess the current environmental risk of the equipment. Specifically, Φ k (qk (t)) is to transform the state feature q according to a specific mapping rule. k (t) is converted into a risk assessment value; for example, q k (t) may be the current device temperature, and Φ k (q k (t)) converts the temperature value into the equipment risk level through a certain function (such as logarithmic function, exponential function, etc.); different mapping functions Φ k This can take different forms depending on the nature of the state characteristics; common choices are:

[0087] Exponential mapping: When changes in state characteristics have an exponential impact on risk, e.g. Applicable to situations where rapid changes in status bring high risks;

[0088] Logarithmic mapping: When the growth of the state gradually reduces the impact of risk, such as Φ k (q k (t)) = log(1 + q k (t)), for situations where the risk increases slowly due to changes in status;

[0089] By Φ k (q k (t)), different state characteristics can be more flexibly associated with equipment risks, so that the environmental assessment G(t) can more accurately reflect the comprehensive risk impact of the operating environment and data sensitivity on the equipment;

[0090] Based on the device environment evaluation function G(t), a suitable encryption strategy is selected to deal with different risk levels; the encryption strategy is defined as P(t), which represents the encryption scheme adopted by the device at time t; the encryption strategy is selected by maximizing the cumulative benefit, hoping to obtain the maximum security benefit at both the current moment and the future moment; the specific decision formula is:

[0091]

[0092] Among them, B is the set of all possible encryption strategies, is the instantaneous profit function of the encryption strategy p at the current moment when the environment is evaluated as G(t), which indicates the adaptability of the encryption strategy to the current risk; ζ is the discount factor, which determines the discount rate of future returns. It is the long-term profit function at the next time point, and an optimal encryption strategy is selected in this way.

[0093] Furthermore, in order to ensure that the encryption strategy has appropriate strength in different environments, we introduce the optimization problem of encryption strength; the encryption strength J(t) is proposed, which represents the encryption level adopted by the device at time t. The encryption level is adjusted according to the current state of the device and the expected requirements. The encryption strength is optimized by minimizing the error of the encryption strength adjustment through the Lagrangian optimization model:

[0094]

[0095] Among them, δ represents the adjustment amount of encryption strength, J optimal (t) is the expected encryption strength, which represents the ideal encryption strength at the current time t; is the square term of the encryption strength change rate, which indicates the rate of change of encryption strength over time. During optimization, this term should be controlled not to be too large. λ is the regularization parameter, which controls the rate of change of encryption strength. By optimizing this function, it can be ensured that the change of encryption strength can be adjusted dynamically with the risk, but not too drastic, thereby ensuring data security while improving system performance.

[0096] The data destruction mechanism is usually triggered when the device detects an extremely high risk. In order to respond to different risk situations more flexibly, we introduce a destruction mechanism based on dynamic probability. The trigger condition for data destruction is no longer a simple fixed threshold judgment, but a dynamic probability function is used to calculate whether the device should destroy the data. The destruction trigger probability ρ(t) is defined as:

[0097]

[0098] Among them, α is the growth coefficient, which controls the rate of change of the trigger probability, G threshold is the risk threshold of the destruction mechanism; when the risk assessment value G(t) of the device exceeds this threshold, the trigger probability ρ(t) of data destruction gradually increases, ensuring that sensitive data can be destroyed in a high-risk environment in a timely manner to avoid information leakage;

[0099] At each time point t, the encryption strategy and strength are optimized based on historical data to ensure the accuracy of future decisions. The cumulative utility function U(t) is introduced. Function U(t) integrates the encryption strength J(t) and encryption strategy P(t), and adjusts the current encryption scheme based on historical risk assessment results:

[0100]

[0101] Among them, ρ mis the destruction probability at the mth moment, J(m) is the encryption strength at that moment, and P(m) is the encryption strategy at that moment; the optimization goal is to maximize the cumulative utility U(t), and by optimizing historical data, make future encryption strategies and strength selection more accurate and effectively respond to the ever-changing risk environment; t is the current moment, and m is a variable used to represent the time index;

[0102] In this plan, it is necessary to explain:

[0103] Periodic inspection: By collecting relevant information about the operating environment and data sensitivity, a system state vector Q(t) is formed to reflect the current state of the device;

[0104] Environmental assessment: Calculate the risk level at the current moment by combining data sensitivity and operating environment information through the nonlinear assessment function G(t);

[0105] Encryption level adjustment: Based on G(t), select the appropriate encryption strategy P(t), and adjust the encryption strength J(t) according to the optimization algorithm to ensure that the encryption strength is dynamically adjusted as the environment changes;

[0106] Dynamic encryption processing: The system encrypts data in real time and flexibly adjusts encryption strategies according to different operating environments and data sensitivity, ensuring that data is more strongly encrypted in high-risk environments and avoiding over-encryption in low-risk environments to improve performance.

[0107] As a further solution, a data residual index D is generated based on the data residual information. res ,The data residual index is used to evaluate the data clearing situation in the device; the proposed device contains N data blocks, d i represents the residual state of the i-th data block. If d i =1 means the data block has not been cleared. i = 0 means it has been completely cleared; in order to accurately evaluate the data residue, a formula with nonlinear adjustment is introduced to capture the residual effect of clearing:

[0108]

[0109] Where N is the total number of data blocks in the device, indicating how many data blocks need to be processed; d i is the residual state of the i-th data block; ξ i is the location complexity, which indicates the complexity of the physical or logical storage location of the ith data block, which may affect the ease of clearing; i is the sensitivity coefficient of data block i, reflecting the importance or risk level of the data block. The higher the data sensitivity, the more important the block is; α i is a nonlinear adjustment parameter used to control s iThe intensity of the residual effect, which determines how strongly the residual index responds to changes in sensitive data; θ i Indicates the residual inflection point threshold of the data block. When s i Greater than θ i When , the residual effect changes more significantly; e is a natural constant that appears in the exponential function and is used to describe the nonlinear relationship between sensitivity and inflection point. The role of is to simulate nonlinear changes, that is, when the sensitivity of the data block s i Approaching or exceeding the threshold θ i When the residual effect increases or decreases exponentially;

[0110] Generate a key recovery efficiency index K based on key recovery efficiency information eff ,The key recovery efficiency index is used to evaluate the recovery efficiency of the ,device’s encryption key after it is remotely invalidated by the ,administrator, and simulates the key invalidation and recovery process through time-dependent ,nonlinear feedback;

[0111]

[0112] Where k(t) is the key state function. When the key is valid at time t, k(t) = 1; when the key is invalid, k(t) = 0. response The response time for key revocation to start, indicating the time when the administrator issues the key revocation command; It represents the complexity of the system response, that is, it affects the difficulty of key recovery; η and β are parameters that control the key recovery rate, affecting the degree of influence of key recovery time on recovery efficiency; is a decay function that describes the rate of change of key recovery over time; γ is the influence of the effective state of the control key on the time-dependent feedback function, and log(1+k(t)·γ·t) introduces the time dynamic response of the key validity; dt represents a small time increment in calculus, which refers to the variable integrated over time t; T max is the maximum time window for monitoring the key recovery operation. The recovery operation will monitor whether the key is invalid during this period. This formula evaluates the dynamic behavior of the key expiration process, including response time, system complexity, etc., and then generates a key recovery efficiency index.

[0113] Generate a data coverage depth index O based on the data coverage depth information depth ,The data coverage depth index is used to evaluate the coverage depth of the device when performing data erasure or overwriting operations. The depth index of the overwriting operation is calculated by introducing the number of overwriting times and the risk attenuation function:

[0114]

[0115] where ζ i)t) represents the risk attenuation coefficient of the i-th data block at time t. The overwriting operation will gradually reduce the risk; z i (t) is the number of times the i-th data block is overwritten at time t, indicating how many times the device has performed overwriting operations on the data block; i It is used to adjust the influence of the coverage effect and determine the specific effect of the coverage depth on the overall cleaning effect; κ i Indicates the threshold of the coverage operation. When the number of coverages is z i (t exceeds the threshold κ i When , the marginal benefit of the coverage effect gradually decreases; e is used in this formula to simulate the nonlinear change relationship of the data coverage operation to ensure that the impact of the coverage depth on data security presents an exponential change. This formula accumulates the number of coverage times of each data block and attenuates the coverage effect through a nonlinear function, so that the number of coverage times and coverage depth can effectively reflect the thoroughness of the clearing operation.

[0116] Based on the data residual index D res , key recovery efficiency index K eff and data coverage depth index O depth Generate a comprehensive assessment index of clearance rate C clear ; The final comprehensive evaluation index of clearance rate is generated through nonlinear combination:

[0117]

[0118] Among them, δ1, δ2 and β3 are weight parameters for controlling the influence of each index on the comprehensive evaluation index of clearance rate; α1 is the residual index D of the control data res The rate of change of μ1 affects the sensitivity of the clearance index; μ1 is the threshold of the data residue index, which is used to control the nonlinear effect of data residue on the clearance rate; It represents the square of the key recovery efficiency, reflecting the impact of recovery efficiency on the removal rate; The logarithmic function of the coverage depth reflects the gain effect of the coverage depth on the removal rate. Finally, a comprehensive removal rate evaluation index C is generated through a nonlinear relationship by combining data residue, key recovery efficiency, and data coverage depth. clear , used to accurately evaluate the cleaning effect of the equipment;

[0119] It should be noted that the clearance comprehensive evaluation index C clearThe cleaning effect is evaluated by analyzing different dimensions of device data cleaning. When the index value is close to 1, it means that the cleaning is thorough; when the index is low, the administrator needs to take specific remedial measures. Based on the evaluation results of data residue, key recovery and coverage depth, possible remedial measures include: For data residue problems, administrators can use more thorough coverage algorithms, such as multiple random data overwrites, physical destruction of storage media, etc., to ensure that data cannot be recovered; for the problem of low key recovery efficiency, the automation of the key management system can be strengthened, the response time of key expiration can be shortened, or the key expiration mechanism can be redesigned to prevent data leakage caused by unrecovered keys; for the problem of insufficient data coverage depth, the number of data coverages can be increased, the coverage strength can be improved, or a stronger erasure algorithm can be used, such as multiple write technology, to ensure that data is completely erased and cannot be recovered. Through these precise remedial measures, the device can further improve the security of data cleaning and ensure the comprehensive evaluation index of the cleaning rate. To achieve the ideal level; in addition, if the device cannot be powered on or cannot be turned on, conventional data clearing methods (such as software erasure, remote key recovery, etc.) will not be able to be performed. In this case, the administrator should take physical remedial measures. First, physical destruction methods can be used, such as physical crushing, cutting, or physical destruction of storage media through special equipment to ensure that the data cannot be recovered. Secondly, electromagnetic interference methods or demagnetization equipment are used to perform strong magnetic field treatment on magnetic storage media such as hard disks and solid-state drives to completely erase data. In addition, high-temperature incineration or acid-base chemical treatment can be used to perform high-temperature or chemical corrosion on storage devices to completely destroy their internal storage units. These methods do not rely on device power or operating system and can effectively prevent data leakage when the device cannot be turned on.

[0120] Furthermore, based on the state information Q(t) and the risk assessment result G(t), the system generates a dynamic encryption key. The key generation process is based on a nonlinear function of the device state, and the key is sliced ​​by different state parameters. This key generation method makes the key not only weighted, but also dynamically adjusted as the state changes.

[0121]

[0122] Where K(t) is the dynamic key, which is the encryption key generated at time t according to the state parameters; k i (t) is the slice part of the key, which represents the key fragment generated under a specific state; α i and θ i To control the nonlinear changes of key slices, the key generation has a complex dynamic response; e is a natural constant, which ensures that the nonlinear adjustment of key slices can flexibly respond to the rapid changes in the state. This key generation method ensures the diversity and dynamics of the key and prevents the key from being statically cracked or exposed;

[0123] The encryption strategy is dynamically selected based on the risk assessment result G(t). Each layer of encryption strategy selects the encryption algorithm adaptively and dynamically nests the encryption formula:

[0124] C dyn (M) = ε outer (ε inner (M,K inner (t)),K outer (t))

[0125] Where M is the data packet that needs to be encrypted; ε inner and ε outer They are the inner encryption algorithm and the outer encryption algorithm respectively; K inner (t) and K outer (t) inner encryption key and outer encryption key, respectively, are generated according to different operating environment information and data sensitivity information;

[0126] The levels of risk assessment results include low risk level, medium risk level and high risk level; two risk level thresholds are proposed: threshold τ1 and threshold τ2, which are used to determine the encryption strategy switching standards that the system should adopt when the risk is low, medium and high respectively;

[0127] Low risk level: When the risk level G(t) of the device is lower than the preset threshold τ1, the system selects the basic encryption method, which includes symmetric encryption algorithms, such as AES-128. Symmetric encryption is a method that uses the same key for encryption and decryption. It is suitable for low-risk scenarios and has fast calculation speed and high performance.

[0128] Medium risk level: When the risk level G(t) is between the two preset thresholds τ1 and τ2, the intermediate encryption method is used; the inner layer uses AES-256 symmetric encryption, and the outer layer uses RSA-2048 asymmetric encryption for additional protection; asymmetric encryption uses two different keys for encryption and decryption, namely the public key and the private key, which can increase the security of the data;

[0129] High risk level: When the risk level G(t) exceeds the threshold τ2, it indicates that the device is in a very dangerous environment, and an advanced encryption strategy is adopted. At this time, the inner layer performs multiple chain encryptions (for example, multiple applications of the AES-256 algorithm), and the outer layer uses RSA-4096 encryption, combined with multi-signature technology, to ensure that data is protected at the highest level in a high-risk environment. In summary, the system will dynamically select the most appropriate encryption method based on the results of the device risk assessment, from basic symmetric encryption to complex chain and multi-signature encryption, to ensure that data can be fully protected under different risks.

[0130] According to the transmitted data state vector Q(t) and the key model stored in the key management system, the decryption path is generated:

[0131]

[0132] in and They are the inner decryption function and the outer decryption function respectively; the decryption process will adjust the decryption path according to the state feedback during transmission to ensure correct and secure decryption even in different states;

[0133] To ensure the long-term security of data, the system adopts a dynamic key recovery mechanism and a multi-layer data destruction strategy;

[0134] Key recovery mechanism: The key management system monitors the risk assessment value G(t) and the key recovery efficiency index K eff , determine whether the key needs to be recycled or invalidated; where K eff is the key recovery efficiency index, which represents the time dynamic relationship between key expiration and recovery;

[0135] Data destruction mechanism: If the device detects a physical attack or high-risk state, the system will destroy sensitive data through a data overwrite mechanism to prevent recovery. The specific data destruction formula is as follows:

[0136]

[0137] Among them, clear (M) represents the coverage depth index of the clearing operation, which is used to measure the thoroughness of data destruction; the higher the value, the better the data destruction effect; where ζ i (t) represents the risk attenuation coefficient of the i-th data block at time t; z i (t) is the number of times the i-th data block is covered at time t; λ i Used to adjust the impact of the coverage effect; κ i Indicates the threshold for the overwrite operation.

[0138] A processing system for dynamic encryption of sensitive enterprise data, including a key management system module, a device status monitoring and evaluation module, an encryption dynamic adjustment module, a remote key expiration and data evaluation module, and a data clearance rate evaluation and rating module;

[0139] Key management system module: responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time.

[0140] Equipment status monitoring and assessment module: The operating system or hardware layer within the device regularly checks the system status and establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment;

[0141] Encryption dynamic adjustment module: adjusts the encryption level in real time according to the environmental assessment level, and performs dynamic encryption processing on the data;

[0142] Remote key expiration and data assessment module: When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and assess the clearance rate of the device's remaining data;

[0143] Data clearance rate assessment and rating module: obtains data residue information, key recovery efficiency information, and data coverage depth information through the key management system, and generates a comprehensive clearance rate assessment index for rating processing.

[0144] It should be noted that this solution is a processing method and system for dynamic encryption of sensitive enterprise data, which aims to provide full life cycle encryption management for enterprise devices. First, the enterprise generates and distributes encryption keys for each device through the key management system. When the enterprise device is first activated, it needs to register with the system through a designated channel to obtain a unique encryption key. This process ensures that the encryption key of each device is secure and independent, avoiding the security risks caused by multiple devices using the same key. During the normal use of the device, the key management system is responsible for regularly updating and maintaining the key to ensure the safe use of the key. In addition, the key management system can remotely invalidate the key, especially when the device has a security risk or is about to be scrapped. This mechanism ensures the effective control of the encryption key.

[0145] Next, the device's operating system or hardware layer will regularly collect the device's operating status information, including operating environment information and data sensitivity information; operating environment information includes the device's temperature, network connection status, physical location, etc., which can reflect the device's current external conditions; data sensitivity information involves data importance, access frequency, data type, and potential risk assessment; this information is represented by multi-dimensional vectors, and the system analyzes the device's current environment and data characteristics based on this status information to extract the key features that best reflect the device's security risks; the system will use a multiple weight model to allocate different status information, extract the status features that have the greatest impact on device security, and provide a basis for subsequent encryption policy adjustments;

[0146] After analyzing the device's status information, the system will dynamically adjust the encryption level in real time according to the device's current risk assessment level. Specifically, the system selects an appropriate encryption strategy for the device based on the device's environmental assessment results. If the device is in a low-risk state, the system will select a relatively simple encryption method to reduce unnecessary encryption strength. If the device is in a medium- or high-risk state, the system will automatically increase the encryption complexity and select a more secure encryption algorithm and multi-level encryption method to ensure data transmission and storage security. Dynamic adjustment of the encryption level can ensure that the device can maintain the best data security status in different usage scenarios, avoid system performance degradation caused by excessive encryption, and provide sufficient security protection in high-risk situations.

[0147] When an enterprise confirms that a device needs to be scrapped or a serious security risk has occurred, the administrator can remotely send a command through the key management system to immediately invalidate the device's encryption key to ensure that the data in the device cannot be further accessed; at this time, the system will also start the data clearing assessment program to detect the residual data in the device; through the key management system, the system can obtain information such as the residual data of the device, key recovery efficiency, and data coverage depth, and generate a comprehensive evaluation index for the clearing rate; the generation of the comprehensive evaluation index for the clearing rate can help enterprises quantify the effect of clearing the remaining data on the device, thereby evaluating the thoroughness of data clearing and ensuring that sensitive data in the device is completely cleared when it is scrapped or in a security risk state;

[0148] In general, through the four core modules of key management, device status monitoring, dynamic encryption adjustment and data clearing assessment, the security of sensitive data of enterprise devices is ensured throughout its life cycle; whether it is data encryption during normal use of the device or data clearing when the device is scrapped or faces security threats, the system can provide comprehensive security protection; through dynamic adjustment of encryption strategies and effective key management, the system can flexibly respond to real-time environment and risk situations to ensure that sensitive data of the enterprise is not leaked due to external threats or device failures;

[0149] Sensitive data in an enterprise usually includes business secrets, customer information, financial data, intellectual property, strategic planning, etc. Once leaked, this data may have a serious impact on the competitiveness, reputation and legal compliance of the enterprise. This solution ensures that sensitive data is always in a safe state throughout the life cycle of the device through key management, dynamic encryption and data clearing assessment, especially when the device is discarded or security risks arise, it can effectively prevent data from being accessed or restored without authorization. Discarded devices often still have uncleared data. Through this solution, these residual data can be completely cleared when the device is scrapped to avoid the risk of data leakage.

[0150] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A processing method for dynamic encryption of enterprise sensitive data, characterized in that: include: Establish a key management system, which is responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time. The operating system or hardware layer within the device regularly checks the system status, establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment, adjusts the encryption level in real time based on the environmental assessment level, and dynamically encrypts the data; When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and evaluate the device's remaining data removal rate; The key management system is used to obtain data residue information, key recovery efficiency information, and data coverage depth information, and a comprehensive evaluation index for the clearance rate is generated for rating processing.

2. A processing method for dynamic encryption of enterprise sensitive data according to claim 1, characterized in that: The proposed device collects operation-related status information at each time point t, including operating environment information and data sensitivity information; the status information is represented by a multidimensional vector Q(t), which contains n different state variables, namely: Q(t)={q1(t),q2(t),…,q n (t)} Among them, q i (t) represents the value of the i-th state at time t. By analyzing the state information, a multi-weight distribution model is used to extract the features of the state information. The model reflects the changes of the state under different weights through a multi-component distribution function, and its expression is: In the formula p(Q(t)), p represents the probability density function, which is used to represent the probability distribution of the state information Q(t) at time t, η j is the weight coefficient of the jth state component, represents the probability distribution of the jth component, μ j is the center point of the state component, Δ j is the fluctuation amplitude of the state component; Based on the currently collected state information Q(t), the risk assessment of the environment in which the equipment is located is carried out, and the environmental assessment function is proposed to be G(t) to represent the risk level of the equipment at time t. The contribution of each state variable is expressed as a product in the form of a nonlinear combination as an overall assessment value. The calculation formula of G(t) is: Among them, γ k is the influence coefficient of the kth state feature, indicating the weight of this feature in environmental assessment; Φ k (q k (t)) is the state feature q k (t) mapped to a nonlinear function of risk factors; Based on the device environment evaluation function G(t), an encryption strategy is selected to deal with different risk levels; the encryption strategy is defined as P(t), which represents the encryption scheme adopted by the device at time t; the encryption strategy is selected by maximizing the cumulative benefit, hoping to obtain the maximum security benefit at both the current moment and the future moment; the specific decision formula is: Among them, B is the set of all possible encryption strategies, is the instantaneous profit function of the encryption strategy p at the current moment when the environment is evaluated as G(t), which indicates the adaptability of the encryption strategy to the current risk; ζ is the discount factor, which determines the discount rate of future returns. is the long-term return function at the next time point.

3. A processing method for dynamic encryption of enterprise sensitive data according to claim 2, characterized in that: The encryption strength J(t) is proposed. The encryption strength J(t) represents the encryption level adopted by the device at time t. The encryption level is adjusted according to the current state of the device and the expected requirements. The encryption strength is optimized by minimizing the error of the encryption strength adjustment through the Lagrangian optimization model: Among them, δ represents the adjustment amount of encryption strength, J optimal (t) is the expected encryption strength, which represents the ideal encryption strength at the current time t; is the square term of the rate of change of encryption strength, and λ is the regularization parameter that controls the rate of change of encryption strength; A destruction mechanism based on dynamic probability is introduced. The trigger condition of data destruction is calculated through a dynamic probability function to determine whether the device should destroy the data. The destruction trigger probability ρ(t) is defined as: Among them, α is the growth coefficient, which controls the rate of change of the trigger probability, G threshold is the risk threshold of the destruction mechanism; when the risk assessment value G(t) of the device exceeds this threshold, the trigger probability ρ(t) of data destruction increases, and sensitive data can be destroyed in a high-risk environment; At each time point t, the encryption strategy and strength are optimized based on historical data, and the cumulative utility function U(t) is introduced. Function U(t) integrates the encryption strength J(t) and encryption strategy P(t), and adjusts the current encryption scheme based on historical risk assessment results: Among them, ρ m is the destruction probability at the mth moment, J(m) is the encryption strength at that moment, and P(m) is the encryption strategy at that moment. The optimization goal is to maximize the cumulative utility U(t).

4. A processing method for dynamic encryption of enterprise sensitive data according to claim 3, characterized in that: Generate data residual index D based on data residual information res ,The data residual index is used to evaluate the data clearing situation in the device; the proposed device contains N data blocks, d i represents the residual state of the i-th data block. If d i =1 means the data block has not been cleared. i = 0 means it is completely cleared; the residual effect of clearing is captured by introducing a formula with nonlinear adjustment: Where N is the total number of data blocks in the device, indicating how many data blocks need to be processed; d i is the residual state of the i-th data block; ξ i is the location complexity, which indicates the complexity of the physical or logical storage location of the i-th data block; s i is the sensitivity coefficient of data block i; α i is a nonlinear adjustment parameter used to control s i The intensity of the residual effect; θ i represents the residual inflection point threshold of the data block; e is a natural constant; Generate a key recovery efficiency index K based on key recovery efficiency information eff ,The key recovery efficiency index is used to evaluate the recovery efficiency of the ,device’s encryption key after it is remotely invalidated by the ,administrator, and simulates the key invalidation and recovery process through time-dependent ,nonlinear feedback; Where k(t) is the key state function. When the key is valid at time t, k(t) = 1; when the key is invalid, k(t) = 0. response The response time for key recovery to start; It represents the complexity of the system response, which affects the difficulty of key recovery. η and β are parameters that control the key recovery rate. is a decay function that describes the rate of change of key recovery over time; γ is the influence of the effective state of the control key on the time-dependent feedback function, and log(1+k(t)·γ·t) introduces the time dynamic response of the key validity; T max is the maximum time window for monitoring key recovery operations; Generate a data coverage depth index O based on the data coverage depth information depth ,The data coverage depth index is used to evaluate the coverage depth of the device when performing data erasure or overwriting operations. The depth index of the overwriting operation is calculated by introducing the number of overwriting times and the risk attenuation function: where ζ i (t) represents the risk attenuation coefficient of the i-th data block at time t; z i (t) is the number of times the i-th data block is covered at time t; λ i Used to adjust the impact of the coverage effect; κ i Indicates the threshold for the overwrite operation.

5. A processing method for dynamic encryption of enterprise sensitive data according to claim 4, characterized in that: Based on the data residual index D res , key recovery efficiency index K eff and data coverage depth index O depth Generate a comprehensive assessment index of clearance rate C clear ; The final comprehensive evaluation index of clearance rate is generated through nonlinear combination: Among them, δ1, δ2 and β3 are weight parameters for controlling the influence of each index on the comprehensive evaluation index of clearance rate; α1 is the residual index D of the control data res The rate of change of; μ1 is the threshold value of the data residual index; represents the square of the key recovery efficiency; Represents the logarithmic function of coverage depth.

6. A processing method for dynamic encryption of enterprise sensitive data according to claim 5, characterized in that: Based on the state information Q(t) and the risk assessment result G(t), the system generates a dynamic encryption key. The key generation process is based on a nonlinear function of the device state and slices the key through different state parameters. Where K(t) is the dynamic key, which is the encryption key generated at time t according to the state parameters; k i (t) is the slice part of the key; α i and θ i To control the nonlinear changes of key slices; The encryption strategy is dynamically selected based on the risk assessment result G(t). Each layer of encryption strategy selects the encryption algorithm adaptively and dynamically nests the encryption formula: C dyn (M)=ε outer (ε inner (M,K inner (t)),K outer (t)) Where M is the data packet that needs to be encrypted; ε inner and ε outer They are the inner encryption algorithm and the outer encryption algorithm respectively; K inner (t) and K outer (t) are the inner encryption key and the outer encryption key respectively; The levels of risk assessment results include low risk level, medium risk level and high risk level; Two risk level thresholds are proposed: threshold τ1 and threshold τ2; Low risk level: When the risk level G(t) of the device is lower than the preset threshold τ1, the system selects the basic encryption method, which includes the symmetric encryption algorithm; Medium risk level: When the risk level G(t) is between the two preset thresholds τ1 and τ2, the intermediate encryption method is used; the inner layer uses AES-256 symmetric encryption, and the outer layer uses RSA-2048 asymmetric encryption for additional protection; High risk level: When the risk level G(t) exceeds the threshold τ2, an advanced encryption strategy is adopted. At this time, the inner layer performs multiple chain encryptions, and the outer layer uses RSA-4096 encryption, combined with multi-signature technology to ensure that data is protected in a high-risk environment; According to the transmitted data state vector Q(t) and the key model stored in the key management system, the decryption path is generated: in and They are inner decryption function and outer decryption function respectively; The system adopts a dynamic key recovery mechanism and a multi-layer data destruction strategy; Key recovery mechanism: The key management system monitors the risk assessment value G(t) and the key recovery efficiency index K eff , determine whether the key needs to be recycled or invalidated; Data destruction mechanism: If the device detects a physical attack or high-risk status, the system will destroy sensitive data through a data overwrite mechanism. The specific data destruction formula is as follows: Among them, clear (M) represents the coverage depth index of the clearing operation.

7. The method for dynamic encryption of enterprise sensitive data according to claim 6 further comprises a processing system for dynamic encryption of enterprise sensitive data, characterized in that: It includes key management system module, equipment status monitoring and evaluation module, encryption dynamic adjustment module, remote key expiration and data evaluation module, data clearing rate evaluation and rating module; Key management system module: responsible for generating and distributing encryption keys. Enterprise devices register with the system through channels and obtain encryption keys when they are used for the first time. Equipment status monitoring and assessment module: The operating system or hardware layer within the device regularly checks the system status and establishes the environmental assessment level of the device based on the sensitivity of the data and the operating environment; Encryption dynamic adjustment module: adjusts the encryption level in real time according to the environmental assessment level, and performs dynamic encryption processing on the data; Remote key expiration and data assessment module: When an enterprise confirms that a device is scrapped or a security risk occurs, the administrator can remotely send a command to the device through the key management system to immediately invalidate the device's encryption key and assess the clearance rate of the device's remaining data; Data clearance rate assessment and rating module: obtains data residue information, key recovery efficiency information, and data coverage depth information through the key management system, and generates a comprehensive clearance rate assessment index for rating processing.

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