CN113821617A - Questionnaire survey method, equipment and storage medium - Google Patents
Questionnaire survey method, equipment and storage medium Download PDFInfo
- Publication number
- CN113821617A CN113821617A CN202110990643.5A CN202110990643A CN113821617A CN 113821617 A CN113821617 A CN 113821617A CN 202110990643 A CN202110990643 A CN 202110990643A CN 113821617 A CN113821617 A CN 113821617A
- Authority
- CN
- China
- Prior art keywords
- target
- real
- option
- user
- options
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000009826 distribution Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 33
- 238000007619 statistical method Methods 0.000 claims abstract description 32
- 238000012546 transfer Methods 0.000 claims description 30
- 239000011159 matrix material Substances 0.000 claims description 29
- 238000004891 communication Methods 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 23
- 230000007246 mechanism Effects 0.000 claims description 16
- 238000013507 mapping Methods 0.000 claims description 13
- 230000007704 transition Effects 0.000 claims description 13
- 230000004044 response Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 abstract description 16
- 230000005540 biological transmission Effects 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 14
- 238000005516 engineering process Methods 0.000 description 8
- 238000011835 investigation Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioethics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Software Systems (AREA)
- Human Computer Interaction (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application provides a questionnaire survey method, equipment and a storage medium. In the embodiment of the application, a new questionnaire survey scheme is provided for survey questions providing more than two selectable options, so that differential privacy protection can be performed on questionnaire answers given by a user locally, and data privacy in a data transmission process is fully guaranteed; the statistical analysis work is based on the noisy answers corresponding to the users participating in the questionnaire, so that the real options given by the users cannot be revealed in the statistical analysis result; the unbiased estimation operation can restore the survey result from the overall distribution dimensionality as truly as possible based on the statistical analysis result, and data processing of the single user dimensionality is not involved in the process, so that the privacy of the user data can be guaranteed in each link of the questionnaire survey, the user is encouraged to answer according to the real idea, the questionnaire quality can be effectively improved, and the more valuable survey result can be obtained.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a questionnaire survey method, device, and storage medium.
Background
At present, companies at home and abroad introduce a recommended value (Net Promoter Score, NPS) as an important index into a working environment. The NPS may be a very important criterion to measure whether a product really gets the approval of the user.
Since the NPS survey process may involve problems such as conflict of interests, a login-free survey method is often used to protect the privacy of the user, but the login-free survey method is likely to receive an invalid questionnaire or be attacked maliciously, resulting in poor survey effect.
Disclosure of Invention
Aspects of the present disclosure provide a questionnaire survey method, device, and storage medium for improving authenticity of a survey result while protecting privacy of a user.
The embodiment of the application provides a questionnaire survey method, which comprises the following steps:
acquiring real options given by a target user for a target problem, wherein more than two selectable options are provided in the target problem;
carrying out differential privacy processing on the real option to obtain a denoised answer;
obtaining the noise-added answers of other users aiming at the target question;
and carrying out statistical analysis and unbiased estimation on the noisy answers of the target user and other users to obtain a survey result under the target question.
The embodiment of the application also provides terminal equipment, which comprises a memory, a processor and a communication component;
the memory is to store one or more computer instructions;
the processor, coupled with the memory and the communication component, to execute the one or more computer instructions to:
acquiring real options given by a target user for a target problem, wherein more than two selectable options are provided in the target problem;
carrying out differential privacy processing on the real option to obtain a denoised answer;
and providing the noisy answers to a server through the communication assembly, so that the server performs statistical analysis and unbiased estimation on the noisy answers of the target user and other users to the target problem to obtain a survey result under the target problem.
The embodiment of the application also provides a computing device, which comprises a memory, a processor and a communication component;
the memory is to store one or more computer instructions;
the processor, coupled with the memory and the communication component, to execute the one or more computer instructions to:
receiving a noise-added answer corresponding to at least one user through the communication assembly, wherein the noise-added answer is obtained by performing differential privacy processing on a real answer given by the user for a target question, and the target question provides more than two selectable options;
and carrying out statistical analysis and unbiased estimation on the noisy answers of the at least one user to obtain a survey result under the target question.
Embodiments of the present application also provide a computer-readable storage medium storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the aforementioned questionnaire survey method.
Embodiments of the present application further provide a computer program product, which includes a computer program/instructions, wherein when the computer program is executed by a processor, the processor is caused to implement the steps in the aforementioned questionnaire survey method.
In the embodiment of the application, a new questionnaire survey scheme is provided for survey questions providing more than two selectable options, so that the real options given by a target user for the target questions can be obtained; carrying out differential privacy processing on the real option to obtain a denoised answer; based on the above, the noisy answers of the target user and other users can be subjected to statistical analysis and unbiased estimation to obtain the survey result under the target question. Therefore, in the embodiment of the application, differential privacy protection can be performed on the questionnaire answers given by the user locally, so that the data privacy in the data transmission process is fully ensured; the statistical analysis work is based on the noisy answers corresponding to the users participating in the questionnaire, so that the real options given by the users cannot be revealed in the statistical analysis result; the unbiased estimation operation can restore the survey result from the overall distribution dimensionality as truly as possible based on the statistical analysis result, and data processing of the single user dimensionality is not involved in the process, so that the privacy of the user data can be guaranteed in each link of the questionnaire survey, the user is encouraged to answer according to the real idea, the questionnaire quality can be effectively improved, and the more valuable survey result can be obtained.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a questionnaire survey method according to an exemplary embodiment of the present application;
FIG. 2 is a logical schematic diagram of a questionnaire survey plan provided by an exemplary embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal device according to another exemplary embodiment of the present application;
fig. 4 is a schematic structural diagram of a computing device according to another exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Currently, because a problem such as a benefit conflict may be involved in an NPS investigation process, a login-free investigation method is often adopted to protect user privacy, but the login-free investigation method is prone to receiving an invalid questionnaire or being attacked maliciously, and therefore investigation effect is poor. To this end, in some embodiments of the present application: aiming at survey questions providing more than two selectable options, a new questionnaire survey scheme is provided, so that real options given by a target user aiming at the target questions can be obtained; carrying out differential privacy processing on the real option to obtain a denoised answer; based on the above, the noisy answers of the target user and other users can be subjected to statistical analysis and unbiased estimation to obtain the survey result under the target question. Therefore, in the embodiment of the application, differential privacy protection can be performed on the questionnaire answers given by the user locally, so that the data privacy in the data transmission process is fully ensured; the statistical analysis work is based on the noisy answers corresponding to the users participating in the questionnaire, so that the real options given by the users cannot be revealed in the statistical analysis result; the unbiased estimation operation can restore the survey result from the overall distribution dimensionality as truly as possible based on the statistical analysis result, and data processing of the single user dimensionality is not involved in the process, so that the privacy of the user data can be guaranteed in each link of the questionnaire survey, the user is encouraged to answer according to the real idea, the questionnaire quality can be effectively improved, and the more valuable survey result can be obtained.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a questionnaire survey method according to an exemplary embodiment of the present application. Fig. 2 is a logic diagram of a questionnaire survey plan provided in an exemplary embodiment of the present application. Referring to fig. 1, the method may include:
102, acquiring noisy answers of other users for a target question;
and 103, carrying out statistical analysis and unbiased estimation on the noisy answers of the target user and other users to obtain a survey result under the target question.
The questionnaire survey method provided by the embodiment can be applied to the investigation scene of the multi-value questions. Wherein, the multi-value question refers to a survey question that can provide more than two selectable options. For example, the target question in this embodiment may be a net recommendation NPS question, which may provide more than two scores as selectable options, such as 0-10 scores as selectable options, and the user may select a corresponding score from 0-10 scores according to a real use feeling.
It should be understood that one or more survey questions may exist in the questionnaire, and for convenience of description, the description of the questionnaire survey plan will be made from the perspective of a single survey question in the present embodiment, but in practical applications, the questionnaire survey plan provided by the present embodiment may be used to output the survey results for a plurality of survey questions, respectively, to complete the questionnaire survey.
In this embodiment, the implementation architecture of the questionnaire survey scheme is not specifically limited, and for example, a CS architecture may be adopted, and a server and a terminal device of a user are used to cooperate, and a survey result is output by the server. Of course, the P2P architecture may also be adopted, and the terminal device may output the survey result by using the interaction between the terminal devices of the users. In this embodiment, the description of each step will not be limited to an execution subject, and in different implementation architectures, the relevant step may be adapted to the corresponding execution subject as needed.
In this embodiment, more than two selectable options may be configured for the target issue. Based on this, referring to fig. 1 and 2, in step 100, the target user may give real options for the target question. For example, for the NPS problem, the user may determine his score from 0-10 points based on the experience of use. In this way, the real options given by the target user for the target problem can be obtained. The questionnaire survey scheme provided by the embodiment can encourage the user to answer the questionnaire in the login state, and for this reason, in step 100, the identity information of the target user can be acquired. It should be noted that the login form of the user is not limited in this embodiment. For example, currently, most user terminals may default to log in with a user ID (for example, a user ID in an android phone system or an IOS phone system), and in this login form, an authorized user ID in the user terminal may be obtained as the identity information in this embodiment. For another example, the target user may register and log in a questionnaire survey application (APP or applet) running in the user terminal, and in this login form, in this embodiment, the registration information of the user may be obtained as the identity information. Accordingly, in this embodiment, the identity information of the target user can be obtained, and the association between the real option and the identity information of the target user can be established, so as to mark the user corresponding to the real option.
In view of the fact that the target question may relate to user privacy or conflict of interests, in step 101, the real options given by the target user may be subjected to differential privacy processing to obtain a noisy answer. Differential privacy (differential privacy) is a means in cryptography, and aims to maximize the accuracy of data query and minimize the opportunity of identifying records thereof when performing data query.
In step 101, a differential privacy process may be performed on the real option given by the target user in a local differential privacy manner, for example, the differential privacy process may be performed in a local (i.e., user-side) designated APP or web page H5. After the differential privacy processing, the true option given by the target user cannot be determined from the noisy answer, so that the data privacy of the target user can be effectively protected from the source.
In this way, a noisy answer corresponding to the true choice of the target user may be generated. In addition, in the same way, the noisy answer corresponding to the real option given by other users under the target question can be generated. Based on the above, the noise-added answers of the target user and other users can be used as the survey basis.
Referring to fig. 1 and 2, in step 103, the noisy answers of the target user and other users may be statistically analyzed and unbiased estimated to obtain the survey results under the target question. The statistical analysis operation may include, but is not limited to, counting, distribution, and the like, which is not limited in this embodiment, and in this embodiment, specific logic of the statistical analysis may be configured as needed. For example, for the NPS problem, the number of people or the proportion corresponding to each score may be counted.
And performing statistical analysis on the noisy answers to obtain a statistical result, and performing unbiased estimation on the statistical result to determine a survey result under the target problem.
Therefore, in the embodiment, a new questionnaire survey scheme is provided for survey questions providing more than two selectable options, so that the real options given by the target user for the target questions can be obtained; carrying out differential privacy processing on the real option to obtain a denoised answer; based on the above, the noisy answers of the target user and other users can be subjected to statistical analysis and unbiased estimation to obtain the survey result under the target question. Therefore, in the embodiment of the application, differential privacy protection can be performed on the questionnaire answers given by the user locally, so that the data privacy in the data transmission process is fully ensured; the statistical analysis work is based on the noisy answers corresponding to the users participating in the questionnaire, so that the real options given by the users cannot be revealed in the statistical analysis result; the unbiased estimation operation can restore the survey result from the overall distribution dimensionality as truly as possible based on the statistical analysis result, and data processing of the single user dimensionality is not involved in the process, so that the privacy of the user data can be guaranteed in each link of the questionnaire survey, the user is encouraged to answer according to the real idea, the questionnaire quality can be effectively improved, and the more valuable survey result can be obtained.
In the above or below embodiments, various implementations may be employed to perform differential privacy processing on real options.
In an alternative implementation, the true choice may be noisy according to a random response mechanism to obtain a noisy answer. Here, the random response is a narrow concept, and can be understood as a mechanism for randomly transferring a real option. In this implementation, the noising of the true option may be achieved by randomly shifting the true option to other options.
In the implementation mode, a transfer matrix corresponding to the target problem can be configured, wherein the transfer matrix comprises the probability of mutual transfer among all selectable options; and performing transfer processing on the real options based on the transfer matrix so as to determine post-transfer options from the selectable options provided by the target question as the post-noise answer.
Wherein the transition matrix can be characterized as:
wherein t represents the number of selectable options provided by the target question; puvRepresenting the probability of option v transitioning to option u after differential privacy. In this way, the transition matrix may include probabilities of transitions between the selectable options provided by the target issue.
In addition, the transition matrix should satisfy the differential privacy definition, and in an exemplary scheme, the probability of the transition of the first option to each selectable option provided by the target question may be determined based on the number of selectable options provided by the target question, with the goal that the sum of the probabilities of the transition of the first option to each selectable option provided by the target question is equal to 1 for the first option; transferring the probability of the first option to each selectable option provided by the target problem as a row/column in a transfer matrix; wherein the first option is any one of the selectable options provided by the target question.
This exemplary scheme may determine the values of the elements in the transition matrix as follows:
that is, in the case where u ═ v, the probability value is calculated according to the formula of the first row; and in the case that u ≠ v, calculating a probability value according to an equation of a second row, wherein e is a natural logarithm and epsilon is a differential privacy parameter. In this way, the sum value is 1 for any row in the transition matrix P, so that the differential privacy definition can be satisfied. Of course, in this embodiment, other schemes may also be used to determine the element values in the transition matrix, for example, user specification, and the like.
In this way, it is possible to configure the transition matrix for the target problem. On the basis, the real options of at least one user under the target question can be respectively subjected to transfer processing based on the transfer matrix so as to generate a noisy answer of at least one user. Also in the case of the real option of the target user, the real option of the target user may be subjected to a transfer process based on the transfer matrix, during which the real option of the target user is probabilistically transferred to any selectable option provided by the target question (including the real option itself). For example, for the NPS problem, if the real option given by the target user is 6 points, based on the transition matrix, the 6 points may be shifted to 7 points, 1 point, or 6 points, and it is this randomness, which can ensure the privacy of the noisy answer, that is, the noisy answer does not show the real option of the target user.
In another implementation, the true options of the target user may be noisy according to the laplacian mechanism to obtain a noisy answer.
In this implementation, noise may be generated for the true option based on the laplacian distribution; adding noise to the real option; and according to a preset mapping rule, mapping the real options subjected to noise addition to target options in all selectable options provided by the target question as answers after noise addition.
An exemplary mapping rule may include: and mapping the real options after noise addition to the nearest selectable options.
In an NPS problem scenario, an exemplary mapping rule may be characterized as:
wherein x isiRepresenting the actual option given by the target user, yiDenotes xiCorresponding noise-added answers;
random noise conforming to the laplacian distribution can be represented. Alternatively, to maximize the sum of diagonal elements in the distribution matrix corresponding to the laplacian distribution, we can set cuU +0.5, u ∈ {1,2, … t-1}, t denotes the number of selectable options, and 1,2, u, t denote t selectable options provided by the target question, e.g., scores. In this way, in the event that the noisy real option is not aligned with any of the selectable options, the noisy real option may be mapped to the closest selectable option to it. For example, if the real option is 6 points, laplacian noise may be added for 6 points, and if 6 points after noise addition become 4.3 points, 6 points may be mapped to 4 points. Due to laplace noise
The method has randomness, so that the randomness of the answer after noise addition can be ensured, and the answer after noise addition cannot reveal real options.
In this implementation, an exemplary laplace distribution can be designed as follows:
wherein,
a cumulative distribution function representing a Laplace distribution, t representing the number of selectable options, PuvRepresenting the probability that option v is laplacian mapped to option u. According to different values of u, the probability value can be calculated by using the formula under the corresponding row. Accordingly, a laplacian distribution can be obtained, and the differential privacy definition is met. Of course, in this embodiment, other schemes may be used to determine each element in the laplacian distributionPrime number PuvFor example, the user specifies the mode, and the embodiment is not limited thereto.
Therefore, the real options can be subjected to noise addition based on the Laplace distribution, and the answer after noise addition is obtained.
The two alternative implementations can be distinguished in that the way of adding noise by the laplacian mechanism defaults that adjacent attributes have similar semantics, and the transfer operation in the random response mechanism can not take this similar semantics into account. Therefore, through experiments, a random response mechanism can be adopted to obtain a better investigation result.
It should be noted that, in this embodiment, other implementation manners may also be adopted to perform the difference privacy processing on the real option of the target user, and this embodiment is not limited to the two exemplary implementation manners described above.
On this basis, in the embodiment, the noisy answers of the target user and other users can be subjected to statistical analysis to obtain statistical distribution; carrying out unbiased estimation according to the statistical distribution and the transfer matrix/Laplace distribution to obtain real distribution; and generating a survey result under the target problem based on the real distribution.
Where the statistical distribution can be represented as λ, the true distribution can be represented as π, and the transfer matrix/Laplace distribution can be represented as P, then the equation can exist:
λ＝Pπ
based on this equation, the process of unbiased estimation can be expressed as:
that is, an unbiased estimate of the true distribution π can be obtained based on the statistical distribution λ. In this embodiment, the statistical distribution and the real distribution may include statistical values under each selectable option. For example, for NPS problems, the number of people or the proportion at each score, etc. may be included in the statistical distribution and the true distribution. In this way, the survey results under the desired target problem can be generated from the true distribution.
In the embodiment, the real options can be packaged into the answers after noise addition by performing differential privacy processing on the real options of the target user, so that the answers after noise addition cannot show the real answers provided by the target user, and the privacy of the target user is protected, so that the target user can provide the real answers without worrying about interference of external privacy such as benefit conflict and the like, and the authenticity of the user in the answering stage is improved; the real distribution can be fully restored through unbiased estimation, and the accuracy of the obtained real distribution can be improved in a statistical stage. In this way, the authenticity of the investigation result can be improved from at least the two aspects described above.
It should be noted that the execution subjects of the steps of the methods provided in the above embodiments may be the same device, or different devices may be used as the execution subjects of the methods. For example, the execution subjects of steps 100 to 103 may be device a; for another example, the execution subject of steps 100 and 101 may be device a, and the execution subject of steps 102 and 103 may be device B; and so on.
In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations are included in a specific order, but it should be clearly understood that the operations may be executed out of the order presented herein or in parallel, and the sequence numbers of the operations, such as 101, 102, etc., are merely used for distinguishing different operations, and the sequence numbers do not represent any execution order per se. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel.
As mentioned above, the present embodiment is not limited to the real-time architecture of the questionnaire survey scheme described above, and the questionnaire survey scheme will be described below from the dimensions of the server and the terminal device used by the user, respectively, taking the CS architecture as an example.
Fig. 3 is a schematic structural diagram of a terminal device according to another exemplary embodiment of the present application. As shown in fig. 3, the terminal device includes: memory 30, processor 31, and communications component 32.
A processor 31, coupled to the memory 30, for executing the computer program in the memory 30 for:
acquiring real options given by a target user for a target problem, wherein more than two selectable options are provided in the target problem;
carrying out differential privacy processing on the real option to obtain a denoised answer;
and providing the noisy answers to the server through the communication component, so that the server performs statistical analysis and unbiased estimation on the noisy answers of the target user and other users to the target problem to obtain a survey result under the target problem.
The terminal device provided by the embodiment is a terminal device used by a target user. The terminal equipment can be used for receiving a questionnaire containing a target question, carrying out differential privacy processing on real options given by a target user locally, and then sending a noise-added answer to a server.
It should be appreciated that other users' terminal devices may perform the same process and provide a noisy answer to the target question to the server. In this way, the server may obtain a noisy answer for at least one user as a basis for the survey.
In an alternative embodiment, the processor 31, in performing the differential privacy processing on the real option to obtain the noisy answer, may be configured to:
according to a random response mechanism, carrying out noise addition on the real option to obtain a noise-added answer; or
And (4) carrying out noise addition on the real option according to a Laplace mechanism to obtain a noise-added answer.
In an alternative embodiment, the processor 31, in performing the noise-adding on the real option according to the random response mechanism to obtain the noise-added answer, may be configured to:
configuring a transfer matrix corresponding to the target problem, wherein the transfer matrix comprises the probability of mutual transfer among all selectable options;
and performing transfer processing on the real options based on the transfer matrix so as to determine post-transfer options from the selectable options provided by the target question as the post-noise answer.
In an alternative embodiment, the processor 31, in configuring the transition matrix corresponding to the target problem, may be configured to:
aiming at the first option, determining the probability of transferring the first option to each selectable option provided by the target question based on the number of the selectable options provided by the target question by taking the sum of the probabilities of transferring the first option to each selectable option provided by the target question as 1;
transferring the probability of the first option to each selectable option provided by the target problem as a row/column in a transfer matrix;
wherein the first option is any one of the selectable options provided by the target question.
In an alternative embodiment, the processor 31, in the process of adding noise to the real option according to the laplacian mechanism to obtain the noise-added answer, may be configured to:
generating noise for the real option based on the laplacian distribution;
adding noise to the real option;
and according to a preset mapping rule, mapping the real options subjected to noise addition to target options in all selectable options provided by the target question as answers after noise addition.
In an alternative embodiment, the mapping rule may include: and mapping the real options after noise addition to the nearest selectable options.
In an alternative embodiment, the target question is a net recommendation NPS question, and the selectable option uses a score.
Further, as shown in fig. 3, the terminal device further includes: display 33, power supply components 34, audio components 35, and the like. Only some of the components are schematically shown in fig. 3, and the terminal device is not meant to include only the components shown in fig. 3.
It should be noted that, for the technical details in the embodiments of the terminal device, reference may be made to the related description in the foregoing method embodiments, and for the sake of brevity, detailed description is not provided herein, but this should not cause a loss of the scope of the present application.
Accordingly, the present application further provides a computer-readable storage medium storing a computer program, where the computer program can implement the above steps that can be executed by a terminal device when executed.
Fig. 4 is a schematic structural diagram of a computing device according to another exemplary embodiment of the present application. Referring to fig. 4, the computing device may include: memory 40, processor 41, and communication component 42.
A processor 41, coupled to the memory 40, for executing the computer program in the memory 40 for:
receiving a noise-added answer corresponding to at least one user through the communication component 42, wherein the noise-added answer is obtained by performing differential privacy processing on a real answer given by the user for a target question, and the target question provides more than two selectable options;
and carrying out statistical analysis and unbiased estimation on the noisy answers of at least one user to obtain a survey result under the target question.
The computing device provided in this embodiment may be a server in the CS architecture, and the computing device may communicate with a terminal device used by each of at least one user and receive a noisy answer provided by the terminal device of the at least one user.
In an alternative embodiment, the processor 41, in performing statistical analysis and unbiased estimation on the noisy answer of at least one user to obtain the survey result under the target question, may be configured to:
carrying out statistical analysis on the noisy answers of the target user and other users to obtain statistical distribution;
carrying out unbiased estimation according to the statistical distribution and the transfer matrix/Laplace distribution to obtain real distribution;
and generating a survey result under the target problem based on the real distribution.
In an alternative embodiment, the target question is a net recommendation NPS question, and the selectable option uses a score.
Further, as shown in fig. 4, the computing device further includes: power supply assembly 43, and the like. Only some of the components are schematically shown in fig. 4, and the computing device is not meant to include only the components shown in fig. 4.
It should be noted that, for the technical details in the embodiments of the computing device, reference may be made to the related description in the foregoing method embodiments, and for the sake of brevity, detailed description is not provided herein, but this should not cause a loss of scope of the present application.
Accordingly, the present application further provides a computer-readable storage medium storing a computer program, where the computer program can implement the above steps that can be executed by a computing device when executed.
Accordingly, embodiments of the present application also provide a computer program product, which includes a computer program/instructions, wherein when the computer program is executed by a processor, the processor is caused to implement the steps in the aforementioned questionnaire survey method. The computer presentation product may be questionnaire survey software, or may be other application software integrating questionnaire survey capabilities, such as instant messaging application software, life service application software, and the like.
The memory of fig. 3 and 4, described above, is used to store computer programs and may be configured to store various other data to support operations on the computing platform. Examples of such data include instructions for any application or method operating on the computing platform, contact data, phonebook data, messages, pictures, videos, and so forth. The memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The communication components of fig. 3 and 4 described above are configured to facilitate wired or wireless communication between the device in which the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a WiFi, a 2G, 3G, 4G/LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
The display of fig. 3 includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The power supply components of fig. 3 and 4 described above provide power to the various components of the device in which the power supply components are located. The power components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device in which the power component is located.
The audio component of fig. 3 described above may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive an external audio signal when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (12)
1. A questionnaire survey method comprising:
acquiring real options given by a target user for a target problem, wherein more than two selectable options are provided in the target problem;
carrying out differential privacy processing on the real option to obtain a denoised answer;
obtaining the noise-added answers of other users aiming at the target question;
and carrying out statistical analysis and unbiased estimation on the noisy answers of the target user and other users to obtain a survey result under the target question.
2. The method of claim 1, the differentially privacy processing the real option to obtain a noisy answer, comprising:
according to a random response mechanism, carrying out noise addition on the real option to obtain a noise-added answer; or
And carrying out noise addition on the real option according to a Laplace mechanism to obtain the noise-added answer.
3. The method of claim 2, said noising said real option in accordance with a random response mechanism to obtain said noisy answer, comprising:
configuring a transfer matrix corresponding to the target problem, wherein the transfer matrix comprises the probability of mutual transfer among all selectable options;
and performing transfer processing on the real options based on the transfer matrix so as to determine post-transfer options from the selectable options provided by the target question as the post-noise answer.
4. The method of claim 3, the configuring the transition matrix corresponding to the target issue, comprising:
aiming at a first option, determining the probability of transferring the first option to each selectable option provided by the target question based on the number of the selectable options provided by the target question by taking the sum of the probabilities of transferring the first option to each selectable option provided by the target question to be equal to 1;
transferring the probability of the first option to each selectable option provided by the target problem as a row/column in the transfer matrix;
wherein the first option is any one of the selectable options provided for the target question.
5. The method of claim 2, the noising the real option according to the laplacian mechanism to obtain the noisy answer, comprising:
generating noise for the true option based on a laplacian distribution;
adding the noise to the real option;
and according to a preset mapping rule, mapping the real options subjected to noise addition to target options in all selectable options provided by the target question as answers after noise addition.
6. The method of claim 5, the mapping rule comprising: and mapping the real options after noise addition to the nearest selectable options.
7. The method of claim 2, wherein the performing statistical analysis and unbiased estimation on the noisy answers of the target user and other users to obtain the survey results under the target question comprises:
carrying out statistical analysis on the noisy answers of the target user and other users to obtain statistical distribution;
carrying out unbiased estimation according to the statistical distribution and the Laplace distribution under a transfer matrix/Laplace mechanism under a random response mechanism to obtain real distribution;
and generating a survey result under the target question based on the real distribution.
8. The method of claim 1, the target question being a net recommended value, NPS, question, the selectable option employing a score.
9. A terminal device comprising a memory, a processor, and a communication component;
the memory is to store one or more computer instructions;
the processor, coupled with the memory and the communication component, to execute the one or more computer instructions to:
acquiring real options given by a target user for a target problem, wherein more than two selectable options are provided in the target problem;
carrying out differential privacy processing on the real option to obtain a denoised answer;
and providing the noisy answers to a server through the communication assembly, so that the server performs statistical analysis and unbiased estimation on the noisy answers of the target user and other users to the target problem to obtain a survey result under the target problem.
10. A computing device comprising a memory, a processor, and a communication component;
the memory is to store one or more computer instructions;
the processor, coupled with the memory and the communication component, to execute the one or more computer instructions to:
receiving a noise-added answer corresponding to at least one user through the communication assembly, wherein the noise-added answer is obtained by performing differential privacy processing on a real answer given by the user for a target question, and the target question provides more than two selectable options;
and carrying out statistical analysis and unbiased estimation on the noisy answers of the at least one user to obtain a survey result under the target question.
11. A computer-readable storage medium storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the questionnaire survey method of any of claims 1-8.
12. A computer program product comprising computer programs/instructions, wherein the computer programs, when executed by a processor, cause the processor to carry out the steps of the questionnaire survey method of any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110990643.5A CN113821617A (en) | 2021-08-26 | 2021-08-26 | Questionnaire survey method, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110990643.5A CN113821617A (en) | 2021-08-26 | 2021-08-26 | Questionnaire survey method, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113821617A true CN113821617A (en) | 2021-12-21 |
Family
ID=78913567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110990643.5A Pending CN113821617A (en) | 2021-08-26 | 2021-08-26 | Questionnaire survey method, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113821617A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117056390A (en) * | 2023-10-12 | 2023-11-14 | 之江实验室 | Investigation method and device for sensitive problems, storage medium and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140137260A1 (en) * | 2012-11-14 | 2014-05-15 | Mitsubishi Electric Research Laboratories, Inc. | Privacy Preserving Statistical Analysis for Distributed Databases |
| CN112380567A (en) * | 2020-11-27 | 2021-02-19 | 南京航空航天大学 | Investigation method with confidence based on localized differential privacy |
| CN112885468A (en) * | 2021-01-26 | 2021-06-01 | 深圳大学 | Teacher consensus aggregation learning method based on random response differential privacy technology |
-
2021
- 2021-08-26 CN CN202110990643.5A patent/CN113821617A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140137260A1 (en) * | 2012-11-14 | 2014-05-15 | Mitsubishi Electric Research Laboratories, Inc. | Privacy Preserving Statistical Analysis for Distributed Databases |
| CN112380567A (en) * | 2020-11-27 | 2021-02-19 | 南京航空航天大学 | Investigation method with confidence based on localized differential privacy |
| CN112885468A (en) * | 2021-01-26 | 2021-06-01 | 深圳大学 | Teacher consensus aggregation learning method based on random response differential privacy technology |
Non-Patent Citations (1)
| Title |
|---|
| 彭长根;赵园园;樊玫玫;: "基于最大信息系数的主成分分析差分隐私数据发布算法", 信息网络安全, no. 02, 10 February 2020 (2020-02-10) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117056390A (en) * | 2023-10-12 | 2023-11-14 | 之江实验室 | Investigation method and device for sensitive problems, storage medium and electronic equipment |
| CN117056390B (en) * | 2023-10-12 | 2024-02-02 | 之江实验室 | Investigation method and device for sensitive problems, storage medium and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Olejnik et al. | Smarper: Context-aware and automatic runtime-permissions for mobile devices | |
| CN110011954B (en) | Homomorphic encryption-based biological identification method, device, terminal and business server | |
| CN107026842B (en) | Method and device for generating security problem and verifying identity | |
| US9003556B2 (en) | Techniques for in-app user data authorization | |
| KR102267270B1 (en) | Incentive-based app execution | |
| US20200257749A1 (en) | Method and a device for friend recommendation | |
| CN105701122A (en) | Log collection method, apparatus and system | |
| WO2013012640A2 (en) | Suggesting invitations to join a network | |
| US20170091303A1 (en) | Client-Side Web Usage Data Collection | |
| Zhu et al. | Understanding identity exposure in pervasive computing environments | |
| CN105210343A (en) | Establishing communication | |
| CN112446736A (en) | Click through rate CTR prediction method and device | |
| CN113821617A (en) | Questionnaire survey method, equipment and storage medium | |
| US20220091873A1 (en) | Systems and methods for cross media reporting by fast merging of data sources | |
| Weng et al. | A mobile computing technology foresight study with scenario planning approach | |
| CN105224870A (en) | Suspected virus applies the method and apparatus uploaded | |
| CN117349671A (en) | Model training method and device, storage medium and electronic equipment | |
| CN110705635B (en) | Method and apparatus for generating an isolated forest | |
| CN114036564A (en) | Construction method of private data derivative graph | |
| CN114638998A (en) | Model updating method, device, system and equipment | |
| CN113312542B (en) | Processing method and device of object recommendation model and electronic equipment | |
| US20190335326A1 (en) | Aggregating network cell data to address user privacy | |
| CN110647767A (en) | Data viewing method, electronic device and computer-readable storage medium | |
| CN116600020B (en) | Protocol generation method, terminal cloud collaborative recommendation method and device | |
| CN117134911B (en) | Secret sharing method, secret segmentation terminal, secret recovery terminal, system and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40070299 Country of ref document: HK |
|
| TA01 | Transfer of patent application right |
Effective date of registration: 20240312 Address after: # 03-06, Lai Zan Da Building 1, 51 Belarusian Road, Singapore Applicant after: Alibaba Innovation Co. Country or region after: Singapore Address before: Room 01, 45th Floor, AXA Building, 8 Shanton Road, Singapore Applicant before: Alibaba Singapore Holdings Ltd. Country or region before: Singapore |
|
| TA01 | Transfer of patent application right |