CN107395646B - A User Behavior Privacy Protection Method Against CSI Time-Frequency Domain Information Attacks - Google Patents

Abstract

The invention discloses a user behavior privacy protection method against CSI time-frequency domain information attack. The method firstly detects the SNR value of user behavior privacy through a mobile phone to judge whether the current area is safe, if not, the user walks The data in the process is used as known data, and the location of the malicious device is calculated according to the signal attenuation model and corrected in real time. The present invention has been verified by actual tests, and it is proved that the estimated number of steps, step lengths and directions of the present invention are relatively accurate. Compared with the same type of positioning methods using signal attenuation models, the positioning results of this method can achieve the same accuracy. For the real-time guidance of the user, we can prove that this method can effectively protect the user's private information, and the user's surfing experience will not be affected when the user is in a safe area.

Description

A User Behavior Privacy Protection Method Against CSI Time-Frequency Domain Information Attacks

technical field

The invention relates to the technical field of information security, in particular to a method for protecting user behavior privacy against CSI time-frequency domain information attacks.

Background technique

WiFi is now an important part of the Internet of Things. In recent years, WiFi has been used for indoor positioning, target tracking, gesture recognition, key detection, lip recognition, etc. However, WiFi can also leak users' privacy.

Nowadays, most mobile phones use graphics and digital passwords to unlock. The security field finds that this method is not safe to unlock. It is very easy to obtain channel status information through a pair of transceivers, and to crack the password entered by the user without the user's knowledge. , and then pose a great threat to user privacy, especially when using Alipay or WeChat payment, if the password is cracked and identified, it will cause serious economic and property losses. For example, the WiPass system, which can identify the user's gesture through the WiFi signal without controlling the user's mobile phone, can crack the password entered by the user, even in a scene without light. The WiHear system enables the disabled to interact with the device only through language commands, allowing the device to do what he wants, which greatly facilitates the life of the disabled. Because the WiHear system can be implemented with the current wireless signal transmitter, when the WiHear system is used in some private places, such as a wireless router in a company meeting, the content of the company meeting is likely to be obtained by attackers. Then the attacker may obtain the company's trade secrets. If these trade secrets are leaked, it will cause immeasurable economic losses to the company. The WiKey system implements fine-grained key detection. If an attacker installs a wireless signal transmitter in the company's senior leadership office, the information entered by the leader on the computer is likely to be obtained by the attacker.

Almost all mobile phones now have built-in acceleration sensors and orientation sensors, and based on the open and open source features of Android, developers can easily develop software to obtain sensor data and store it in real time. The motion information of the target can be measured by motion sensors such as accelerometers, magnetometers, and gyroscopes in the smartphone. Through this information, the heading and distance of the target can be calculated. Combined with its initial position, the position of the target can be calculated.

SUMMARY OF THE INVENTION

Aiming at the problems existing in the prior art, the purpose of the present invention is to provide a user behavior privacy protection method for CSI time-frequency domain information attack, and guide the user to enter the privacy information through the mobile phone after arriving at the safe area, so as to avoid the privacy information from being steal.

In order to realize the above-mentioned tasks, the present invention adopts the following technical solutions:

A method for protecting user behavior privacy against CSI time-frequency domain information attack, comprising the following steps:

Step 1, determine whether the current location is a safe area

After the user walks into a public place and reaches a certain location, the SNR value of the current location is calculated by the following formula:

In the above formula, csi _m is the average value of the CSI collected by the mobile phone when the user swipes the screen to the right on the screen, csi _c is the average value of the CSI collected by the mobile phone within a period of time before the user swipes the screen, and noise is in csi _m Included noise value;

Determine whether the SNR value calculated by the above formula is greater than the set threshold. If it is greater than the threshold, then the current location is not a safe area, and the next step is performed; otherwise, the user can input privacy information through the mobile phone at the current location;

Step 2. Locate the malicious device

Record the user's position when he first entered the public place as the initial position, and calculate the user's steps, step length and each step through the information collected by the built-in sensor of the mobile phone during the period when the user walked from the initial position to the stated position. the position coordinates of one step;

计算公式为：According to the RSSI value obtained by the user at the position of the i-th step, calculate the proportion of the malicious device from the position of this step

The calculation formula is:

rss_i为用户在第i步位置处获取到的RSSI值，

为恶意设备到第i步位置处的距离，n为路径损耗参数，取值为2～9；In the above formula,

rss _i is the RSSI value obtained by the user at the i-th step,

is the distance from the malicious device to the i-th step, n is the path loss parameter, which ranges from 2 to 9;

Record the position of each step of the user as a known reference point, and calculate a series of position coordinates of the malicious device at the i-th step according to the coordinates of the known reference point; then according to the known reference point and the position coordinates of the malicious device , recalculate the ratio d ₁ /d _i of the malicious device from the i-th step;

和d₁/d_i，从所述的一系列位置坐标中确定恶意设备的实际位置坐标；according to

and d ₁ /d _i , determine the actual location coordinates of the malicious device from the series of location coordinates;

Step 3, real-time guidance in the safe area

Calculate the angle θ between the user's current location and the malicious device;

Determine the angular range of the user's walking direction, namely: [θ+90°, θ+270°]

Guide the user to walk toward the stated angle range to reach the safe area.

Further, when there are multiple malicious devices, calculate the angle between each malicious device and the current location of the user, then calculate the intersection of the angle ranges corresponding to each malicious device, and then guide the user to walk toward the angle range of the intersection. .

Further, the coordinate system relative to the position coordinates in step 2 takes the initial position of the user as the origin, the east is the positive direction of the X-axis, the north is the positive direction of the Y-axis, and the direction perpendicular to the XY plane and away from the ground is Z. axis coordinate system.

Further, in the second step, according to the coordinates of the known reference point, the formula for calculating a series of position coordinates of the malicious device at the i-th position is:

Aθ=B Equation 3

in:

(x_i,y_i,z_i)为用户在第i步的位置坐标，i∈(2,m)；n为路径损耗参数，取值为2～9；In the above formula, (X, Y, Z) are the location coordinates of the malicious device,

(x _i , y _i , z _i ) is the position coordinate of the user at the i-th step, i∈(2,m); n is the path loss parameter, ranging from 2 to 9;

When n in Equation 3 takes different values, a series of location coordinates (X _n , Y _n , Z _n ) of malicious devices can be calculated.

Further, in the second step, the formula for recalculating the ratio d ₁ /d _i from the malicious device to the i-th position is:

Further, the method for determining the actual location coordinates of the malicious device is:

At the i-th step, when n takes different values, formulas 2 and 4 respectively calculate different values, and then determine the optimal n value n _opt according to the following formula:

Then the coordinate position of the malicious device corresponding to the optimal n value is the actual position of the malicious device.

The present invention has the following technical characteristics:

The present invention has been verified by actual tests, and it is proved that the estimated number of steps, step lengths and directions of the present invention are relatively accurate. Compared with the same type of positioning methods using signal attenuation models, the positioning results of this method can achieve the same accuracy. For the real-time guidance of the user, we can prove that this method can effectively protect the user's private information, and the user's surfing experience will not be affected when the user is in a safe area.

Description of drawings

Figure 1 shows several situations in which there is a risk of privacy leakage when users use mobile phones when malicious devices exist in the environment, wherein (a) is a schematic diagram of an in-band signal attack, (b) is a schematic diagram of an out-of-band signal attack, and (c) is an outdoor scene A schematic diagram of a malicious device attack, (d) is a schematic diagram of an attack by multiple malicious devices;

Figure 2 is a graph of the curve relationship between the SNR value and the attack success rate;

Fig. 3 is the curve diagram of synthetic acceleration with time;

Fig. 4 is the schematic diagram when the user walks to different positions when the malicious device position is determined;

FIG. 5 is a schematic diagram of the actual location of the malicious device calculated by the user at different locations;

6 is a schematic diagram of guiding a user to walk within a range when there are multiple malicious devices;

FIG. 7 is a schematic diagram when the safe area is guided;

Fig. 8 is the relation diagram of the SNR value of the gait in the user's walking process and the SNR value of the user's input privacy information;

Detailed ways

As shown in Figure 1, after the user arrives in a certain area, if there are WIFI transmitter and receiver in the area, when the user is located close to the device deployed by the attacker, the attacker can use it to receive The terminal obtains the CSI value when the user enters the private information, and then combines with its attack knowledge base, the attacker can easily crack the user's private information, such as unlock password, Alipay payment password, WeChat payment password, and the user has privacy leakage. risks of. The present invention provides a privacy behavior protection method that can detect whether the current area is safe, and if it is not safe, guide the user to reach the safe area, the details are as follows:

A method for protecting user behavior privacy against CSI time-frequency domain information attack, comprising the following steps:

Step 1, determine whether the current location is a safe area

Install the csi tool acquisition software in the user's mobile phone. The software collects the csi value every fixed time, such as 1s; when the user walks into a public place and reaches a certain position, the SNR value of the current position is calculated by the following formula :

In the above formula, csi _m is the average value of the CSI collected by the mobile phone when the user swipes the screen to the right on the screen, csi _c is the average value of the CSI collected by the mobile phone within a period of time before the user swipes the screen, and noise is in csi _m Included noise value. Determine whether the SNR value calculated by the above formula is greater than the set threshold. If it is greater than the threshold, the current location is not a safe area, and the next step is performed; otherwise, the user can input privacy information through the mobile phone at the current location.

In this step, the SNR value is used to judge whether the current location is safe or not. The judgment principle is: the inventor team found that there is a linear regression relationship between the SNR value and the success rate, and the SNR value can be used to evaluate the success rate of the attacker. The time for a user to input private information is generally a few seconds, and the received csi per second is generally 100 to 2000 (related to the sampling rate). The csi value used in the above calculation is the average value of the received csi.

According to the experimental experience, we can obtain the relationship between the SNR value and the attack success rate r as shown in the following formula:

r=(p ₁ ×SNR+p ₂ )/((SNR) ³ +q ₁ ×(SNR) ² +q ₂ ×SNR+q ₃ )

In the above formula, p ₁ , p ₂ , q ₁ , q ₂ , and q ₃ are constant parameters, and the SNR value is the SNR value obtained by formula 1. The inventor obtained the SNR value and the attack success rate by experimenting with 15 kinds of pattern passwords at different distances, and then used the above formula to fit the SNR value and the attack success rate to establish a relationship. The fitted curve is shown in the figure. 2 shown. Through the SNR value and the attack success rate obtained by the experiment, we can obtain the values of the above constant parameters, p ₁ =1081, p ₂ =543.9, q ₁ =-1033, q ₂ =4657, q ₃ =2582.

In order to determine whether the current location is safe, the method adopts the method of simulated input, that is, the user slides the screen from left to right with his finger, and uses this action to simulate the user inputting information on the screen of the mobile phone. In this process, the above-mentioned _csim can be collected. The user generally does nothing for a few seconds before entering the private information. In this method, the _csic collects the CSI value during this period. From the amplitude curve of the CSI value, it can be seen which period is the static time and which is the private information input time. The CSI amplitude of the static time is stable and remains on a horizontal line, while the private information input period The magnitude of the CSI fluctuates. The static time can be determined by the amplitude stability of the CSI before the user inputs the privacy information, or after the user inputs the privacy information, generally 1s to 3s. When calculating in Equation 1, a period of time before sliding the screen can be 1s to 3s, and the average value of the CSI collected during this period is taken as _csic . The SNR value can be calculated by formula 1, and then the probability that the user may be successfully attacked at the location is calculated according to the relationship between the SNR value and the success rate. When the probability of being successfully attacked is greater than a threshold, it means that the current area is not safe If there is a risk of privacy leakage, the following method is used to guide the user to enter the security area before entering the privacy information; if it is less than the threshold, it means that the risk of privacy leakage in the current area is very small, and the user can enter the privacy information through the mobile phone at the current location. According to the actual test experience, the threshold can be set to -2 in this scheme.

Step 2. Locate the malicious device

The principle used in the second step is as follows:

Because there is a relationship between the RSSI value and the propagation distance, and the RSSI value can be obtained directly by the mobile phone, in the present invention, we use the signal propagation model to locate the malicious location. The signal propagation model formula is as follows:

d=10 ^{(|RSSI|-A)/(10*n)}

Among them: d is the calculated distance, RSSI is the received signal strength, A is the signal strength when the transmitter (malicious device) and the receiver (user's mobile phone) are separated by 1m, n is the environmental attenuation factor, and the value range of n is 2 ~9. However, when entering a public place, since the sender is an attacker, the above A and n are unknown. We need to obtain A and n in advance, so we can use the user's walking information after entering a public place to obtain unknown parameters, and then perform positioning.

Remember the position when the user first entered the public place as the initial position, and the information collected by the built-in sensors (accelerometer, magnetometer, gyroscope, etc.) , calculate the user's number of steps, step length and the position coordinates of each step; the details are as follows:

In this solution, the initial position (origin) can be used as the initial position (origin) when the user first enters the public place, and the XYZ coordinate system can be established. The east is the positive direction of the X-axis, and the north is the positive direction of the Y-axis, which is perpendicular to the XY plane and away from the ground. The direction is the Z axis.

(1) Estimation of the number of steps

In order to estimate the distance traveled by the user, it is first necessary to estimate the number of steps the user has traveled. When the difference between the estimated number of steps and the actual number of steps traveled is large, there will be a large error in estimating the walking distance for short distances in the experiment. Therefore, it is necessary to accurately estimate the number of steps taken by the user.

During the walking process of the user holding the mobile phone, the ups and downs of the footsteps will have a certain effect on the acceleration sensor. If the acceleration sensor data in a single direction is selected, it is too one-sided, because the user's body fluctuates in multiple directions during walking, so it is necessary to consider the acceleration sensor data in multiple directions, so it is proposed to use the composite acceleration to estimate the number of steps , the specific calculation formula of the composite acceleration is:

In the above formula, X, Y, Z are the data in the three directions of the acceleration sensor X, Y, and Z respectively; after calculating the composite acceleration, find the peak value of the composite acceleration (which can be realized by findpeaks), and the number of peaks found is the step number, as shown in Figure 3.

By comparing the accuracy of estimating the number of steps between the acceleration sensor data and the synthetic acceleration data, it is concluded that the synthetic acceleration can be used to estimate the number of steps taken by the user more easily, so the synthetic acceleration is used to estimate the number of steps. In order to improve the accuracy of step estimation and reduce the large error in step estimation due to human factors such as rapid shaking of the mobile phone and other abnormal behaviors, it is necessary to set a reasonable peak range according to the actual situation when using synthetic acceleration for step estimation. Similarly, the walking frequency of people cannot be very high, so it is necessary to set the minimum distance between the two peaks, and through these two limitations, the accuracy of step estimation can be improved and the error of step estimation can be reduced.

(2) Step size estimation

After the number of steps taken by the user can be obtained through step (1), the step length of each step of the user needs to be calculated, and the distance traveled by the user can be obtained only by estimating the number of steps. The present invention uses the following formula to estimate the step size:

In the above formula: Y _i is the step size of the ith step, k is the coefficient, and its value is between 0 and 1; max _i is the maximum value of the synthetic acceleration of the ith step, and min _i is the minimum value of the synthetic acceleration of the ith step.

The total distance traveled by the user is the sum of the step lengths of each step. So far, the distance traveled by the user can be obtained, and the user's walking direction needs to be obtained to locate the position coordinates of the user relative to the starting point.

(3) The position coordinates of each step

The present invention uses the following formula to determine the position coordinates of each step of the user:

为第i步当前方向与Y轴的夹角，L_i为第i步的步长，N_i为第i步北向(Y轴)坐标，N_i-1为第i-1步北向(Y轴)坐标，即这里计算出来的每一步的位置坐标是相对于上述XYZ坐标系而言的。Among them, E _i is the east (X-axis) coordinate of the i-th step, and E _i-1 is the east-direction (X-axis) coordinate of the i-1th step,

is the angle between the current direction of the i-th step and the Y-axis, Li is the step size of the _{i-th step, N i is} the north (Y-axis) coordinate of the i-th step, and N _i-1 is the i-1-th step north (Y-axis). ) coordinates, that is, the position coordinates of each step calculated here are relative to the above XYZ coordinate system.

The coordinates of the user's walking process are shown in Figure 4. Figure 4 shows that the user took 5 steps and turned a corner at the fourth step. We can regard the initial point where the user starts walking as (x ₁ , The points after 0,0) can be calculated according to the number of steps, the step size and the data obtained from the gyroscope, as shown in the figure. x ₁ represents the first location point where the user walks.

The present invention uses the data during the user's walking process as known data, specifically calculates the location of the malicious device according to the signal attenuation model, and corrects the location in real time.

计算公式为：According to the RSSI value obtained by the user at the position of the i-th step, calculate the proportion of the malicious device from the position of this step

The calculation formula is:

rss_i为用户在第i步位置处获取到的RSSI值，

为恶意设备到第i步位置处的距离，n为路径损耗参数，取值为2～9；In the above formula,

rss _i is the RSSI value obtained by the user at the i-th step,

is the distance from the malicious device to the position of the i-th step, n is the path loss parameter, which ranges from 2 to 9;

The position of each step of the user is recorded as a known reference point, and according to the coordinates of the known reference point, a series of position coordinates of the malicious device at the i-th step are calculated, specifically:

Aθ=B Equation 3

in:

(x_i,y_i,z_i)为用户在第i步的位置坐标，i∈(2,m)；n为路径损耗参数，取值为2～9；上述参数在一个未知的公共场所中，只有n是未知的，其他参数都可与通过手机传感器来获得，然而n是有范围的，为2～9之间，当取一个n值，例如3.25，我们即可算得恶意设备的位置坐标。In the above formula, (X, Y, Z) are the location coordinates of the malicious device,

(x _i , y _i , z _i ) are the coordinates of the user's position at the i-th step, i∈(2,m); n is the path loss parameter, ranging from 2 to 9; the above parameters are in an unknown public place , only n is unknown, other parameters can be obtained through mobile phone sensors, but n has a range, between 2 and 9, when taking a value of n, such as 3.25, we can calculate the location coordinates of the malicious device .

When n in Equation 3 takes different values, a series of location coordinates (X _n , Y _n , Z _n ) of malicious devices can be calculated, and the most accurate one needs to be selected from these location coordinates as the user’s The actual location of the malicious device determined at i location.

Next, it is necessary to re-determine the ratio d ₁ /d _i of the malicious device from the i-th position:

The meanings of the parameters in formula 4 are the same as before.

At the i-th step, when n takes different values, formulas 2 and 4 respectively calculate different values, and then determine the optimal n value n _opt according to the following formula:

Then, the coordinate position of the malicious device corresponding to the optimal n value in the series of position coordinates of the malicious device is the actual position (X ₀ , Y ₀ , Z ₀ ) of the malicious device. In this scheme, a grid strategy can be adopted for the value of n, and the coordinate position of the corresponding malicious device can be calculated from 2 to 9 by adding 0.05 each time, so as to select the most accurate position.

Because there is an error in the RSSI measurement value, the RSSI value measured in a certain step may have a large error. If the data of this step is used as a reference point for calculation, the calculated location of the malicious device will be inaccurate.

Through experience, we know that when the number of known reference nodes is 4, the positioning error can meet the requirements. Therefore, in the present invention, according to the method in step 2, the position of 4 consecutive steps is calculated as the malicious corresponding to the reference node. The actual location of the device and then averaging these locations can make the location of the malicious device more accurate, as shown in Figure 5. For example, if the user takes 8 steps, the position of steps 1, 2, 3, and 4 can be used as a reference node to obtain a position of a malicious device, and the position of steps 2, 3, 4, and 5 can also be used as a reference node to obtain a malicious device. A position of the device, and then the average value of the obtained position is calculated as the final position coordinate of the malicious device.

Step 3, real-time guidance in the safe area

Calculate the angle θ between the user's current location and the malicious device in the XYZ coordinate system. The calculation formula is:

In the above formula, (X ₀ , Y ₀ ) are the location coordinates of the malicious device, and ( _xi , y _i ) are the current location coordinates of the user (ignoring the angle in the Z-axis direction).

Determine the angular range of the user's walking direction, namely: [θ+90°, θ+270°].

Guide the user to walk toward the stated angle range to reach the safe area. When there are multiple malicious devices, the angle between each malicious device and the user's current location is calculated separately, then the intersection of the angle ranges corresponding to each malicious device is calculated, and the user is guided to walk toward the angle range of the intersection. For example, if the angle between the user and the malicious device 1 is 30 degrees, the direction it should go is [120°, 300°], and the angle between the user and the malicious device 2 is 120 degrees, then the direction it should go is [240°] , 360°]&&[0°, 30°], find the intersection of the above two sets, the intersection is [240°, 300°], then the direction angle to the user is [240°, 300°], as shown in the figure 6 shown. If the user does not follow the prescribed route, repeat the above steps to guide in real time.

Collect the CSI values generated by the user's gait during walking, and then calculate the SNR value of the gait. According to the SNR value of the gait, the SNR value of the private information (that is, the action of sliding the screen to the right) is estimated, and then the SNR of the private information is estimated according to the SNR value of the private information. The value determines whether the current location is a safe area. If it is a safe area, it will prompt the user to stop walking. If it is a non-safe area, otherwise continue to guide the user.

The principle of this step is as follows:

After estimating the location coordinates of the malicious device, the system can guide the user in a safe direction based on the relative position of the user and the malicious device. As shown in Figure 6, when the user is standing at point P, if there is only one malicious AP1 (malicious device) in the space, then the user can directly walk in the opposite direction, and if there are multiple malicious APs, The relative positions of the user and multiple malicious APs need to be considered. As shown in the figure, the user can go in the direction of T1, T2, T5, and T6.

After the system gives the user a safe direction, the user can walk in the direction given by this method, but sometimes, the direction given by the system is not the direction the user really wants to go, so the system needs to combine the sensor data to give the user real-time guidance , as shown in Figure 7.

When the user walks to the safe area, the system can infer the SNR value of the privacy information in real time according to the relationship between the SNR value of the user's walking process and the SNR value when the user enters the privacy information, and then further infer the probability of successful attack. , when the probability is less than a certain threshold, we consider the place where the user walks to be safe.

The relationship between the SNR value of the user's gait and the SNR value of the user's input privacy information during walking is shown in Figure 8. We can see from the figure that the ratio is in the range of 1. Therefore, in the present invention, we can directly Use the ratio 1, that is, the SNR value generated by the gait is regarded as the SNR value generated when the user swipes the screen to the right (inputting privacy information), to judge whether the current position is safe according to the method of step 1.

Claims (6)

1. A user behavior privacy protection method aiming at CSI time-frequency domain information attack is characterized by comprising the following steps:

step one, judging whether the current position is a safe area or not

After a user walks into a public place and arrives at a certain position, calculating a signal-to-noise ratio (SNR) value of the current position by the following formula:

in the above formula, csi_mIs the average value of the Channel State Information (CSI), CSI, collected by the mobile phone when the user slides the screen to the right through the finger_cThe average value of CSI collected by the mobile phone in a period of time before the user slides the screen is shown as CSI_mThe noise value contained in (1);

judging whether the SNR value calculated by the formula is larger than a set threshold value, if so, judging that the current position is not a safe area, and executing the next step; otherwise, the user can input the privacy information at the current position through the mobile phone;

secondly, positioning the malicious equipment

Recording the position of a user just entering a public place as an initial position, and calculating the step number, the step length and the position coordinate of each step of the user through information acquired by a built-in sensor of the mobile phone in the time when the user walks from the initial position to the position;

calculating the proportion of the malicious equipment to the step position according to the Received Signal Strength (RSSI) value acquired by the user at the step position

The calculation formula is as follows:

in the above formula, the first and second carbon atoms are,

rss_ifor the RSSI value obtained by the user at the ith step location,

is a malicious device toThe distance at the position of the ith step is n, and the value of n is 2-9, wherein n is a path loss parameter;

recording the position of each step of the user as a known reference point, and calculating a series of position coordinates of the malicious equipment at the position of the ith step according to the coordinates of the known reference point; then, according to the known reference point and the position coordinates of the malicious equipment, the proportion d of the malicious equipment to the ith step position is recalculated₁/d_i；

According to

And d₁/d_iDetermining actual location coordinates of the malicious device from the series of location coordinates;

step three, the safe area guides in real time

Calculating an angle theta between the current position of the user and the malicious equipment;

determining the angular range of the walking direction of the user, namely: [ theta +90 DEG, theta +270 DEG ]

And guiding the user to walk towards the angle range, so that the safety area can be reached.

2. The method for protecting user behavior privacy against CSI time-frequency domain information attack as claimed in claim 1, wherein when there are multiple malicious devices, the angle of the current location of each malicious device and the user is calculated respectively, then the intersection of the angle ranges corresponding to each malicious device is calculated, and then the user is guided to walk towards the intersection angle range.

3. The method according to claim 1, wherein the coordinate system to which the position coordinates in step two are relative is a coordinate system in which an initial position of the user is an origin, a positive direction of an X axis is toward the east, a positive direction of a Y axis is toward the north, and a direction perpendicular to an XY plane and away from the ground is a Z axis.

4. The method for protecting user behavior privacy against CSI time-frequency domain information attack as claimed in claim 1, wherein in step two, the formula for calculating a series of location coordinates of the malicious device at the ith step location according to the coordinates of the known reference point is:

a θ ═ B (formula 3)

Wherein:

in the above equation, (X, Y, Z) is the position coordinates of the malicious device,

(x_i,y_i,z_i) The position coordinate of the user in the ith step is represented by i ∈ (2, m), and m is the total step number of the user; n is a path loss parameter, and the value of n is 2-9;

when n in equation 3 takes different values, a series of location coordinates (X) of the malicious device can be calculated_n,Y_n,Z_n)。

5. The method for protecting privacy of user behaviors with respect to CSI time-frequency domain information attack as claimed in claim 1, wherein in step two, the ratio d of malicious device to the ith step position is recalculated₁/d_iThe formula of (1) is:

6. the method for protecting user behavior privacy against CSI time-frequency domain information attack as claimed in claim 1, wherein the method for determining the actual location coordinate of the malicious device is:

at the ith step position, when n takes different values, equations 2 and 4 are respectively calculatedDifferent values, and then determining the optimal n value n according to the following formula_opt：

The coordinate position of the malicious device corresponding to the optimal n value is the actual position of the malicious device.

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