Authority management method and system based on user behaviors

Abstract

The invention discloses an authority management method and system based on user behaviors, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, obtaining behavior data of a power user, and carrying out the preliminary data cleaning of the data; s2, performing multi-dimensional feature extraction on the cleaned behavior data, and generating a user behavior portrait in combination with a user identity tag; s3, inputting the user behavior portrait into a behavior scoring model, and outputting a behavior score and a risk coefficient; and S4, dynamically configuring an authority level by an authority control engine according to a scoring risk mapping rule. By introducing a dynamic permission adjustment mechanism based on the user behavior score and risk assessment, the behavior mode of the user can be monitored and analyzed in real time, the permission level of the user can be dynamically adjusted according to the real-time demand and risk level of the user, the permission demand of the user can be judged according to the actual operation behavior of the user, and the user experience is improved. Excessive or insufficient authority authorization is avoided, and intelligence and dynamics of authority management are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and more specifically, to a method and system for permission management based on user behavior. Background Art

[0002] In traditional role-based access control (RBAC) systems, although widely used in organizations, their limitations are very obvious. Since roles and permissions are configured based on static rules, the system fails to consider the diversity and real-time changes of users' actual operation behaviors, often leading to permission abuse, misoperations, and security risks. In addition, traditional permission control methods ignore system environment changes and the context information of user behaviors, and also fail to effectively consider the assessment of users' risk behaviors, making the permission control too rigid to adapt to complex security requirements in a timely manner.

[0003] In recent years, with the development of intelligent technologies, more and more research has begun to explore how to optimize permission management through dynamic and personalized user behavior data. For example, by analyzing specific behaviors of users in the system, such as login frequency, operation patterns, and abnormal behaviors, the real needs and operation risks of users can be evaluated, thereby realizing dynamic permission allocation based on behaviors. Such methods can not only improve system security but also greatly enhance the user experience, ensuring the rationality and accuracy of permissions. However, in the actual application of existing such methods in power grids (such as energy management platforms and power dispatching systems), due to the diversity of electricity users' behaviors, there are still problems of insufficient intelligence and lack of dynamics, such as: different users may have completely different usage habits, response efficiencies, and misoperation tendencies under the same role, and the RBAC model lacks the ability to perceive and adjust this, so problems such as permission redundancy, operation out of control, and abuse risks may occur in the system, especially in security-sensitive business scenarios, the potential risks are particularly prominent. In addition, the RBAC model usually fails to incorporate context information (such as the current network status, device health status, external environment, etc.), and it is difficult to achieve effective dynamic permission adjustment in complex systems with high real-time requirements. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for permission management based on user behavior to solve the problems raised in the above background art:

[0005] A method for permission management based on user behavior includes the following steps:

[0006] S1. Obtain the behavior data of electricity users and perform preliminary data cleaning on the data;

[0007] S2. Extract multi-dimensional features from the cleaned behavior data and generate a user behavior portrait in combination with user identity tags;

[0008] S3. Input the user behavior profile into the behavior scoring model to output the behavior score and risk coefficient;

[0009] S4. Dynamically configure the permission level by the permission control engine according to the scoring risk mapping rule;

[0010] S5. The permission level is used to control the operable range of the user in the energy management platform and the power dispatching system, and can implement hierarchical authorization, auditing, and traceability.

[0011] Preferably, the user behavior profile in S2 includes: electricity consumption pattern characteristics, energy-saving execution score, response delay level, operation stability score, and dispatching coordination index.

[0012] Preferably, the electricity consumption pattern characteristics are used to evaluate the periodicity and volatility of the user's electricity consumption:

[0013]

[0014] where represents the daily average power, represents the daily power standard deviation;

[0015] The energy-saving execution score is used to measure whether the user participates in the energy-saving plan or peak-shaving instruction as required:

[0016]

[0017] where represents the number of times of successfully executing the energy-saving control in the dispatching response, represents the total number of energy-saving control instructions issued by the system;

[0018] The response delay level measures the average response speed of the user after receiving the dispatching command:

[0019]

[0020] where represents the number of dispatching instructions, represents the th response time (seconds) of the dispatching command, represents the maximum response time (seconds) allowed by the system;

[0021] The operation stability score reflects the reliability and misoperation rate of the device control operation:

[0022]

[0023] where represents the number of misoperations, represents the total number of operations;

[0024] The scheduling coordination index reflects the user's ability to cooperate and respond with other users (group load response):

[0025]

[0026] Among them, represents the number of times the user effectively cooperates in group scheduling, represents the number of requests for the user to participate in group scheduling.

[0027] Preferably, the final behavior portrait is represented as a feature vector:

[0028]

[0029] Among them, all eigenvalues .

[0030] Preferably, the comprehensive behavior score S in S3 is calculated as follows:

[0031]

[0032] Among them, the weight is set by the policy configuration manager based on business importance;

[0033] Risk coefficient R:

[0034]

[0035] Among them represents the number of abnormal behaviors in the recent observation period, represents the total number of behavior records in the observation period, represents the standard deviation of the behavior portrait vector , reflecting the degree of behavior fluctuation, represents the weight factor, recommended value: .

[0036] Preferably, a behavior context awareness mechanism is introduced before S4 to judge whether the behavior constitutes a misoperation or an occasional behavior according to the input variables; if the behavior is identified as a misoperation or an occasional behavior, it enters the observation period, records the fault tolerance log, and suspends the permission adjustment; if the abnormality persists during the observation period, the permission downgrading policy is executed; if the behavior is not determined to be a misoperation, or is verified as a high-risk behavior during the observation period, the permission level is dynamically configured by the permission control engine according to the scoring risk mapping rule.

[0037] Preferably, the input variables of the behavior context awareness mechanism include: meteorological information, device operation status, historical scheduling frequency, operation period, terminal status, and user behavior frequency.

[0038] A privilege management system based on user behavior, comprising:

[0039] A behavior collection module, configured to obtain the behavior data of electricity users and perform preliminary data cleaning on the data;

[0040] A portrait modeling module, configured to perform multi-dimensional feature extraction on the cleaned behavior data and generate a user behavior portrait in combination with user identity tags;

[0041] A scoring engine, configured to input the user behavior portrait into a behavior scoring model and output a behavior score and a risk coefficient;

[0042] A privilege control engine, configured to dynamically configure privilege levels according to a scoring risk mapping rule.

[0043] Preferably, the system further includes a context awareness module, which is interface-connected to a meteorological platform, a load monitoring system, and a power equipment status monitoring system to obtain real-time context data for behavior analysis. The context awareness module is configured to determine whether a behavior constitutes a misoperation or an occasional behavior according to the current system load, device status, scheduling plan, weather information, etc.; if the behavior is identified as a misoperation or an occasional behavior, it enters an observation period, records a fault tolerance log, and suspends privilege adjustment; if the abnormality persists during the observation period, a privilege downgrading policy is executed; if the behavior is not determined to be a misoperation, or is verified as a high-risk behavior during the observation period, the privilege control engine dynamically configures the privilege level according to the scoring risk mapping rule.

[0044] Preferably, the system further includes a privilege execution module, which supports remote blocking, dynamic privilege distribution, behavior auditing, and user application backtracking interfaces to ensure the prudence and reasonableness of privilege changes.

[0045] Compared with the prior art, the advantages of the present invention are as follows:

[0046] (1) By introducing a dynamic privilege adjustment mechanism based on user behavior scoring and risk assessment, the present invention can monitor and analyze the behavior patterns of users in real time, and dynamically adjust their privilege levels according to the real-time needs and risk levels of users, and can judge the privilege requirements of users according to their actual operation behaviors, avoiding excessive or insufficient privilege authorization, and improving the intelligence and dynamics of privilege management.

[0047] (2) By combining user behavior scoring and risk coefficients, the present invention monitors and manages users in real time, can timely discover and limit potential risk behaviors, and significantly reduces the risks of privilege abuse and system attacks.

[0048] (3) Through personalized permission control based on user behavior, the present invention can intelligently adjust permissions according to the actual needs and behavior characteristics of users. It not only avoids the limitations brought by fixed roles or permission scopes in traditional permission management methods, but also can optimize the permission configuration according to changes in user behavior and environment, meet the permission requirements of different users in different scenarios, and improve the user experience.

[0049] (4) The present invention further introduces a context awareness mechanism, which can combine real-time context data, such as user behavior history, device status, external environment, etc., to accurately identify the true intention and risk level behind user behavior. This precise permission control can effectively reduce the occurrence of misoperations, and provide detailed auditing and tracing functions for the system, ensuring that every permission change can be clearly recorded, audited, and traced, enhancing the transparency and traceability of the system. Description of the Drawings

[0050] Figure 1 It is a flowchart of the permission management method based on user behavior of the present invention. Detailed Embodiments

[0051] Embodiment, refer to Figure 1 , a permission management method based on user behavior, including the following steps:

[0052] S1. Obtain the behavior data of electricity users and perform preliminary data cleaning on the data;

[0053] S2. Extract multi-dimensional features from the cleaned behavior data and generate a user behavior portrait in combination with user identity tags;

[0054] S3. Input the user behavior portrait into the behavior scoring model and output the behavior score and risk coefficient;

[0055] S4. Dynamically configure the permission level by the permission control engine according to the scoring risk mapping rule;

[0056] S5. The permission level is used to control the operable range of users in the energy management platform and the power dispatching system, and can achieve hierarchical authorization, auditing, and tracing.

[0057] The user behavior portrait in S2 includes: electricity consumption pattern characteristics, energy conservation execution score, response delay level, operation stability score, and scheduling coordination index.

[0058] The electricity consumption pattern characteristics are used to evaluate the periodicity and volatility of user electricity consumption:

[0059]

[0060] where represents the daily average power, represents the standard deviation of daily power; the higher the stability (smaller the standard deviation), the closer the score is to 1.

[0061] The energy-saving execution score is used to measure whether the user participates in the energy-saving plan or peak-shaving instruction as required:

[0062]

[0063] where represents the number of times of successful execution of energy-saving control in the scheduling response, represents the total number of energy-saving control instructions issued by the system, and the higher the response execution rate, the higher the score ( ).

[0064] The response latency level measures the average response speed of the user after receiving the scheduling command:

[0065]

[0066] where represents the number of scheduling instructions, represents the th response time (in seconds) of the scheduling command, represents the maximum response time (in seconds) allowed by the system. The faster the response, the slower the exponential decay, and the closer the score is to 1.

[0067] The operation stability score reflects the reliability and misoperation rate of device control operations:

[0068]

[0069] where represents the number of misoperations, represents the total number of operations. The fewer the misoperations, the higher the score.

[0070] The scheduling coordination index reflects the user's ability to cooperate with other users in a coordinated manner (group load response):

[0071]

[0072] where, represents the number of times the user effectively cooperates in group scheduling, represents the number of requests for the user to participate in group scheduling; the higher the degree of cooperation in the group, the higher the coordination score.

[0073] The final behavior portrait is represented as a feature vector:

[0074]

[0075] Among them, all eigenvalues .

[0076] The comprehensive behavior score S in S3 is calculated as follows:

[0077]

[0078] Among them, the weight is set by the policy configuration manager based on business importance;

[0079] Risk coefficient R:

[0080]

[0081] Among them represents the number of abnormal behaviors in the recent observation period, represents the total number of behavior records in the observation period, represents the standard deviation of the behavior portrait vector , reflecting the degree of behavior fluctuation, represents the weight factor, recommended value: .

[0082] Finally, these scoring results replace the unified role permissions in traditional RBAC, realizing the differential management of user permissions for the same role.

[0083] Before S4, a behavior context awareness mechanism is introduced to judge whether the behavior constitutes a misoperation or an occasional behavior according to the input variables; if the behavior is identified as a misoperation or an occasional behavior, it enters the observation period, the fault tolerance log is recorded, and the permission adjustment is postponed; if there is continuous abnormality during the observation period, the permission downgrading policy is executed; if the behavior is not determined to be a misoperation, or is verified as a high-risk behavior during the observation period, the permission level is dynamically configured by the permission control engine according to the scoring risk mapping rule.

[0084] Step 1: Input user behavior metrics

[0085] Receive the following input data: behavior score ; risk coefficient ;

[0086] Step 2: Define the scoring risk mapping matrix as shown in Table 1

[0087] Construct a two-dimensional scoring-risk mapping table and define the permission levels (P0 - P4):

[0088]

[0089] Table 1

[0090] Explanation of permission levels: P0: Blocked (all active operations are prohibited); P1: Read-only (only data can be viewed); P2: Restricted (some operations require authorization); P3: Normal (conventional operation permissions); P4: Full control (all operations and automatic control are open).

[0091] Step 3: Determine whether the current permission status is consistent with the target permission

[0092] Let the current permission level be , and calculate the target level as . If

[0093] remains unchanged when, if not consistent, continue to execute Step 4;

[0094] Step 4: Set the permission adjustment strategy (including fault tolerance and buffering)

[0095] 4.1 Determine whether there is an observation period. If the user is marked as "under observation" under the misoperation fault tolerance mechanism, then: if the target level is lower than the current level, only record the change request and do not immediately downgrade the permission; start the observation timer (it can be restored if there are no new exceptions within 24 hours);

[0096] 4.2 Start the buffered downgrading mechanism to prevent drastic changes. If the system sets a permission change throttling threshold (e.g., the maximum change per day is 1 level), then decrease / increase the permission level by level;

[0097] Step 5: Execute the permission level change operation. According to the permission change instruction, call the following sub-modules through the permission control engine:

[0098] Permission publisher: Update the permission policy to the user terminal and platform account

[0099] Logger: Record the change source, change reason, and timestamp

[0100] Notification center: Send a permission change notice and reason prompt to the user

[0101] Recovery trigger: Automatically attempt to restore the permission level when the behavior returns to normal

[0102] Step 6: Continuously monitor and periodically evaluate. Recalculate S and R every 5 / 15 / 30 minutes (configurable), perform real-time dynamic maintenance on the permission level, and trigger real-time permission adjustment in combination with abnormal events (such as out-of-bounds strong control, device anomalies, etc.).

[0103] The input variables of the behavior context awareness mechanism include: meteorological information, device operating status, historical scheduling frequency, operation time period, terminal status, and user behavior frequency.

[0104] The misoperation recognition module sets a fault tolerance threshold. If the number of misoperations within a unit time does not exceed the preset value, the original permission level is maintained and a log is recorded.

[0105] The duration of the observation period mechanism and the behavior monitoring frequency are dynamically adjusted according to the user risk level. If the behavior remains stable during the observation period, the original permissions will be automatically restored.

[0106] The permission control engine supports five-level permission configuration: P0 blacklist block, P1 read-only permission, P2 operation requiring authorization, P3 normal operation permission, P4 full control permission.

[0107] A user behavior-based permission management system includes: a behavior collection module, a portrait modeling module, a scoring engine, a context awareness module, a permission control engine, a permission execution module, and a log recording and tracing module.

[0108] The context awareness module is interfaced with a meteorological platform, a load monitoring system, and a power equipment status monitoring system to obtain real-time context data for behavior analysis.

[0109] The permission execution module supports remote blocking, dynamic permission distribution, behavior auditing, and user application tracing interfaces to ensure the prudence and rationality of permission changes.

[0110] The core function of the behavior collection module is to collect various behavior data of users in real time. These behavior data include, but are not limited to, power consumption curves, device operation logs, dispatching response records, out-of-bounds control behaviors, abnormal alarm histories, and collaborative control indicators.

[0111] Data source: The behavior collection module regularly obtains users' power consumption data through device interfaces such as sensors and smart meters. At the same time, the module collaborates with the device management system, the dispatching control system, etc. to collect real-time operation logs and response records.

[0112] Data collection frequency: The module configures the data collection period, with a default of once per minute; for important events, such as system anomalies or user behavior changes, a second-level collection function is provided.

[0113] Power consumption data (such as power curves) is stored in a time-series data format indexed by timestamps, supporting high-frequency data collection and storage.

[0114] Device operation logs, abnormal alarms, dispatching responses, etc. are stored in JSON format for easy subsequent parsing and processing.

[0115] The portrait modeling module constructs a user behavior portrait and generates corresponding behavior feature vectors based on the collected behavior data through data mining and machine learning algorithms.

[0116] Data cleaning and preprocessing: Clean the collected raw data, remove outliers and noise data, and smooth the time series data.

[0117] Feature engineering: Extract important features from user behavior data, such as: electricity consumption regularity, energy-saving execution score, response timeliness, operation stability, scheduling coordination, etc., and through standardization processing, normalize the data to the [0,1] interval.

[0118] Modeling method: Adopt machine learning models based on decision trees, random forests, etc., combine historical data with user behavior patterns, and train a behavior portrait model suitable for this user group.

[0119] Model update mechanism: According to the long-term behavior changes of users, retrain the behavior portrait model regularly or based on abnormal events.

[0120] The portrait feature vectors are stored in a distributed database to ensure efficient retrieval and update.

[0121] The update cycle and trigger mechanism of the user portrait can be customized. For example, it can be updated regularly every month, or triggered for real-time update when important changes occur in user behavior.

[0122] The scoring engine generates the user's behavior score and risk coefficient through the input behavior feature vectors.

[0123] The context awareness module obtains real-time external context data (such as meteorological information, power load data, etc.), combines it with user behavior data, and provides more accurate behavior analysis and risk assessment.

[0124] Data source interface: The context awareness module interfaces with the following platforms for data:

[0125] Meteorological platform: Obtain weather forecast information, temperature and humidity, wind speed and other data, and analyze the impact of weather on electricity consumption behavior.

[0126] Load monitoring system: Obtain grid load information, judge the correlation between user behavior and grid load, and predict the risks brought by load fluctuations.

[0127] Equipment status monitoring system: Obtain the operation status information of power equipment, and analyze the impact of equipment health status on user behavior.

[0128] Data fusion and processing: Perform multi-dimensional data fusion on the above external context information and user behavior data, and predict and warn user behavior based on big data analysis models.

[0129] The data interaction between the context awareness module and external systems is carried out in the RESTful API manner, supporting real-time data acquisition and periodic update.

[0130] Based on the behavior scores and risk coefficients output by the scoring engine, the permission control engine dynamically configures the user permission levels to ensure system security and flexibility.

[0131] Permission mapping rules: Based on scores and risk coefficients, the control engine generates target permission levels through predefined mapping rules (such as a score - risk matrix).

[0132] Permission adjustment mechanism: The engine triggers permission changes through set rules and interfaces with the user devices and system interfaces to issue permission adjustment instructions in real - time.

[0133] Gradual adjustment: When the user scores change little, the permission adjustment adopts a gradual strategy to prevent drastic fluctuations.

[0134] Abnormal monitoring: When abnormal user behavior occurs, the system automatically enters an observation period, and after a certain period, permission restoration or downgrading operations are carried out.

[0135] The permission execution module is responsible for actually executing permission change operations, including remote blocking, dynamic permission distribution, behavior auditing, and traceability interfaces, etc., to ensure the prudence and rationality of permission changes.

[0136] Permission distribution: Through the interface between the permission management system and the user terminal, the permission changes are distributed to the user devices in real - time.

[0137] Remote blocking: For high - risk users, blocking operations are executed to prohibit users from performing any operations.

[0138] Behavior auditing: The permission execution module records all permission change logs and supports user requests for retrospective queries to ensure the transparency and traceability of permission changes.

[0139] Remote control interface: Communicates with the user terminal to execute permission control instructions.

[0140] Audit log interface: Supports the audit query interface, records all operations and change behaviors, and provides a visual query interface.

[0141] The log recording and traceability module is used to record user behavior data, permission change history, abnormal events, etc., to provide complete auditing and traceability capabilities for permission management.

[0142] Log collection: The module real - time collects events such as user behavior data, system warnings, and permission changes, and stores them in a distributed log system.

[0143] Traceability function: Users can query permission change records within a specific time range and view the detailed reasons for each change.

[0144] The log data is stored in a combined manner of a distributed database and a time series database to ensure efficient query and persistent storage.

[0145] The foregoing has shown and described the basic principles, main features and advantages of the present invention; those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed; the scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A permission management method based on user behavior, characterized in that It includes the following steps: S1. Obtain the behavior data of electricity users and perform preliminary data cleaning on the data; S2. Extract multi-dimensional features from the cleaned behavior data, and generate a user behavior portrait in combination with user identity tags; the user behavior portrait includes: electricity consumption pattern features, energy-saving execution scores, response latency levels, operation stability scores, and scheduling coordination indicators; S3. Input the user behavior portrait into the behavior scoring model, and output the behavior score and risk coefficient; S4. Dynamically configure the permission level by the permission control engine according to the scoring risk mapping rule.

2. The rights management method based on user behavior according to claim 1, characterized in that, The described electricity consumption regular feature For evaluating the periodicity and volatility of user electricity consumption: ， in represents the daily average power, represents the daily power standard deviation; The energy-saving execution score used to measure whether the user participates in the energy-saving plan or peak-shaving order as required: ， in Indicates the number of times energy-saving control is successfully executed in the scheduling response. Indicates the total number of energy-saving control instructions issued by the system; The response delay level measures the average response speed after the user receives the scheduling command: ， in Indicates the number of scheduling instructions, Indicates the The response time of the scheduling command, Indicates the maximum response time allowed by the system; The operation stability score reflects the reliability of the device control operation and the misoperation rate: ， wherein represents the number of misoperations, represents the total number of operations; The scheduling coordination index reflects the collaborative response ability of the user with other users: ， Among them, represents the number of times the user effectively collaborates in group scheduling, represents the number of requests for the user to participate in group scheduling.

3. The privilege management method based on user behavior according to claim 2, wherein The final behavior portrait is represented as a feature vector : ， Among them, all eigenvalues .

4. A user behavior-based permission management method according to claim 3, characterized in that, The comprehensive behavior score S in S3 is calculated as follows: ， Among them, the weight is set by the policy configuration management personnel based on business importance Risk coefficient R: ， Among them represents the number of abnormal behaviors within the near observation period, represents the total number of behavior records within the observation period, represents the behavior portrait vector standard deviation of, reflecting the degree of behavior fluctuation, represents the weight factor, recommended value: .

5. A permission management method based on user behavior according to claim 1, characterized in that, Before S4, a behavior context awareness mechanism is introduced to judge whether the behavior constitutes a misoperation or an occasional behavior according to the input variables; if the behavior is identified as a misoperation or an occasional behavior, it enters the observation period, the fault tolerance log is recorded, and the permission adjustment is postponed; if the abnormality persists during the observation period, the permission downgrading policy is executed; If the behavior is not determined to be a misoperation, or is verified as a high-risk behavior during the observation period, the permission control engine dynamically configures the permission level according to the scoring risk mapping rule.

6. The method for privilege management based on user behavior according to claim 5, characterized in that, The input variables of the behavior context awareness mechanism include: meteorological information, equipment operation status, historical scheduling frequency, operation time period, terminal status, and user behavior frequency.

7. A user behavior-based rights management system, used to implement the user behavior-based rights management method according to any one of claims 1 to 4, characterized in that: It includes: A behavior acquisition module, which is used to obtain the behavior data of electricity users and perform preliminary data cleaning on the data; A portrait modeling module, which is used to extract multi-dimensional features from the cleaned behavior data and generate a user behavior portrait in combination with user identity tags; A scoring engine, which is used to input the user behavior portrait into the behavior scoring model and output the behavior score and risk coefficient; A permission control engine, which is used to dynamically configure the permission level according to the scoring risk mapping rule.

8. The privilege management system based on user behavior according to claim 7, wherein, The system also includes a context awareness module, which is docked with the meteorological platform, load monitoring system, and power equipment status monitoring system interface to obtain real-time context data for behavior analysis. The context awareness module is used to judge whether the behavior constitutes a misoperation or an occasional behavior according to the current system load, equipment status, scheduling plan, weather information, etc.; If the behavior is identified as a misoperation or an occasional behavior, it enters the observation period, the fault tolerance log is recorded, and the permission adjustment is postponed; if the abnormality persists during the observation period, the permission downgrading policy is executed; If the behavior is not determined to be a misoperation, or is verified as a high-risk behavior during the observation period, the permission control engine dynamically configures the permission level according to the scoring risk mapping rule.

9. A user behavior-based permission management system according to claim 7, characterized in that, The system also includes a permission execution module, which supports remote blocking, dynamic permission distribution, behavior auditing, and user application backtracking interfaces to ensure the prudence and reasonableness of permission changes.

Images