KR102008095B1 - Method and system for managing spatial crowdsourcing task based on privacy-aware grid - Google Patents

Abstract

According to one embodiment of the present invention, provided is a crowdsourcing task management method performed in a crowdsourcing server, which comprises the steps of: assigning a task registered from a requester to a participant; encrypting location data included in result data of a task obtained as the task assigned to the participant is executed; and receiving the insertion of the result data including the encrypted location data into a grid-based index.

Description

Spatial crowdsourcing task management method and system based on privacy-aware grid {METHOD AND SYSTEM FOR MANAGING SPATIAL CROWDSOURCING TASK BASED ON PRIVACY-AWARE GRID}

The description below relates to a spatial crowdsourcing task management technique based on privacy-aware grid.

Due to the rapid development of mobile devices and communication technologies, most mobile devices have powerful processors, various sensors, and large memory. These sophisticated components make mobile devices an important tool for data sensing, communication, and sharing.

Crowdsensing is a category of crowdsourcing. In crowd systems, environmental data is automatically collected from a participant's mobile device using software that communicates with the environmental sensor. In this case, the case where the crowd sensed data includes location information of the crowd sensing system may be defined as a spatial crowd sensing system. In a spatial crowdsense operation, the requestor must register the required data resource type with the centralized spatial crowdsourcing server. The server then assigns the task to the participant. When the participant accepts the assigned task, the participant moves to the task location, and when the participant arrives in the area, the location based task is executed. The collected resources can then be sent to a central crowdsourcing server.

1 is a diagram for explaining a structure of general crowdsourcing in a mobile device. People's involvement in mobile crowdsourcing has raised some issues with security and privacy. For example, some sensed data may include location information, which may implicitly indicate a mobile user's movement. Compared to traditional networks, the security and privacy of mobile crowdsourcing is a more difficult and important issue. Participants will be involved in both mobile crowd sensing and computing. Data sensed for use in crowdsourcing systems includes location and time information. If such data includes not only sensitive information from mobile devices, but also the crowdsourcing participant's personal information, privacy violations can arise.

Network and web-based task publishing allows hackers to control participants' mobile devices and launch attacks. Task-based crowdsourcing will pose significant security issues, especially if the task contains sensitive information. When these tasks are sent to a dynamic group, the size of the group cannot be determined first, making it more difficult to protect privacy than if the task is outsourced to only one server. Therefore, unauthorized access becomes easier.

The mobile user may accept the crowdsourcing task according to the field of interest, location or device. Mobile crowdsourcing can be engaged through a variety of communication networks, such as wireless sensor networks, cellular networks, Wi-Fi, Bluetooth, and vehicle ad hoc networks. Since there are so many devices participating in mobile crowdsourcing, this may generate heterogeneous data.

Current spatial crowdsourcing systems have always allowed participants to reveal actual location information or approximate location. However, disclosure of such location information to the public is a serious privacy violation. For example, hackers can be exposed to many types of threats from identity theft, physical stalking and sensitive information disclosure. Therefore, a location-specific privacy-aware scheme is essential for task assignment and dissemination of sensing results.

A spatial crowdsourcing task management method and system based on privacy-aware grid can be provided.

A crowdsourcing task management method performed at a crowdsourcing server includes assigning a task registered from a requester to a participant; Encrypting location data included in result data of the task obtained as the task assigned to the participant is executed; And receiving result data including the encrypted location data into a grid-based index.

Assigning a task registered from the requester to the participant may include: registering a task including a resource type of data from the requester, and converting the registered task into a task map; And assigning the converted task map to a submap-based task to the participant, wherein the task is a spatial task including at least one MBR, a task execution time information, and query information. The spatial task may be changed to a task map represented by the spatial region, the task execution time information, and the query information.

The step of assigning a task registered from the requester to the participant may include generating a grid-based index for the task registered from the requester, dividing the task area for the registered task into a plurality of grid cells, And assigning each grid number to one grid cell.

Assigning a task registered from the requester to the participant includes registering data related to the participant as the task assigned to the participant is accepted by the participant, and the task assigned to the participant is executed. The encrypting of the location data included in the result data of the obtained task may include performing a task at the location of the moved task as the task assigned to the participant is executed, and identifying the participant's identification information, time information, and the task. And receiving result data including the identification information and location data of the.

Encrypting the position data included in the result data of the task obtained as the task assigned to the participant is executed, the position data included in the result data for the performed task may be divided into a grid cell obtained by dividing an area of the task. It may include the step of changing to the coordinate information.

Encrypting the position data included in the result data of the task obtained as the task assigned to the participant is executed, the position data included in the result data for the performed task is assigned to the grid cell partitioning the area of the task. And changing the given relative position data and generating a grid based index based on the relative position data and the grid number assigned to the grid cell.

Encrypting the participant's location data through a grid-based index can protect the participant's personal information.

When the result data is inserted into the task map, the data existing in the grid is composed of one data set and inserted to shorten the insertion time.

1 is a view for explaining the structure of crowdsourcing in a mobile device.
2 is a diagram for describing a process of managing a spatial task in a crowdsourcing server, according to an exemplary embodiment.
3 is a diagram illustrating a structure of a security grid index in a crowdsourcing server according to an embodiment.
4 is a block diagram illustrating a configuration of a crowdsourcing server according to an embodiment.
5 is a flowchart illustrating a method for managing a spatial crowdsourcing task based on privacy-away grid in a crowdsourcing server according to an embodiment.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings.

2 is a diagram for describing a process of managing a spatial task in a crowdsourcing server, according to an exemplary embodiment.

The crowdsourcing system (server) may manage location privacy-aware based spatial tasks based on encrypted grid indexes. This approach protects the participant's privacy when sending crowd results continuously over a short time or multiple sessions, and ensures the scalability of the crowdsourcing system (hereinafter referred to as the crowdsourcing server). Can be. In addition, even if the number of participants increases in a short time, the crowdsourcing server can manage the spatial task smoothly.

The spatial task management process performed in the crowdsourcing server will be described. Crowdsourcing data can be used for many types of services on open crowdsourcing platforms. For example, real-time spatial crowdsourcing data can be used in an online monitoring system for end users. Historically, spatial crowdsourcing data can be used for various types of map-based analysis services. In order to support this type of real-time map-based service or map-based analysis for a large number of users, crowdsourcing platforms need to transfer part of the map-based report data as soon as possible, thus protecting participants' location information.

The crowdsourcing server according to the embodiment may improve the speed of spatial data insertion and propose a grid-based index for protecting participant location information using grid-based encrypted location data during the spatial task management process. The proposed spatial work management process can be shown in Figure 2.

When a participant accepts a job from the central crowdsourcing server, the participant's geographic information and participant data is added to the crowdsourcing server during each participant registration process. When the participant is in the sensing area, the participant's sensing data can be inserted into the crowdsourcing server using a grid-based security index.

Spatial tasks may include a spatial region S (a rectangle-based region that includes at least one minimum bounding rectangle (MBR)), task execution time information T (t ₁ ≤ T≤ t ₂ ), and spatial query Q. have. This type of spatial task can be changed to a task map where TM = {S, T, Q}. When a requestor registers a TM with the crowdsourcing server, it assigns the task to the participants in the form of sub-map-based tasks based on the crowdsourcing platform of the participant's electronic device (e.g., mobile device). . The participant can send the result data to the cloud sourcing server in the form of RM = {participant ID, time, task ID, location based result}. At this time, the location data used in the RM is encrypted using a grid-based index to protect the participant's location information and to ensure the scalability of the crowdsourcing server.

The requestor may register a data resource type of a specific area required by the requestor with the spatial crowdsourcing server through the crowdsourcing platform of the requester's electronic device. The crowdsourcing server assigns a participant a task that includes a registered data resource type from the requestor. The participant can accept the assigned task from the crowdsourcing server. Participants who accept the assigned task can move to the task location. When the area associated with the location of the task is reached, the location based task can be performed. The result (resource) collected through this may be transmitted to the crowdsourcing server.

Crowdsourcing server proposes a grid-based security index method. It can support tasks that send location-based task reports to the task map so that the crowdsourcing platform can perform online map-based monitoring services. Grid-based security index method is simpler than the existing encryption method because it encrypts only the relative position of grid cells.

The crowdsourcing server may generate a grid index for the query of the task as the task is registered by the service provider (requester). In this case, an area of the task may be divided into a plurality of grid cells, and a unique grid number may be assigned to each of the divided plurality of grid cells. The spatial task TM may include location data formed from grid-based encrypted data. At this time, the encrypted location data can be used in the result transmission process. When inserted into a map based on the result data including the grid-based encrypted location data, data within the same grid is inserted into a single large data set, thereby reducing insertion time.

Referring to FIG. 3, a diagram for describing the structure of a grid-based security index. In the task assignment method for privacy, the security grid index uses encrypted text instead of the existing grid number. In embodiments, an RSA encryption algorithm may be used for performance testing. While the crowdsourcing server assigns tasks to participants, it is considered to maximize the number of tasks assigned and to minimize the cost of the participants. In this case, in order to protect the actual position data of the participant, the position data included in the result data for the task may be changed to the relative position data given to the grid cell in which the task area is divided. Based on the relative position data and the unique grid number assigned to the grid cell, a grid-based index can be generated and transmitted to the crowdsourcing server. In other words, the grid number and relative position coordinates encrypted with respect to the position data included in the result data of the task may be transmitted to the crowdsourcing server. For example, as shown in FIG. 3, grid cells may be configured from G1 to G25, and grid numbers may be assigned to each grid cell, such as G1, G2, ..., G25. In this case, relative position data may be given to each grid cell. In one example, the relative position data for G13 can be HXA2tk, and the position data of participant P1 can be changed to [HXA2tk, x ₁ , y ₂ ].

The crowdsourcing server can assign geometry-based tasks (eg, rectangle-based discovery requests) to participants using secure grid-based indexes. At this time, the geometric coordinates of the task based on the geometry may be first changed to the security grid coordinates and transmitted to the participants. Participant P _i (i is a natural number, i≥1) performs a plurality of tasks (e.g. t1, t2, t3, ..., tj) during the participant's sensing time, and encrypts the position data with secure grid coordinates do. The encrypted grid coordinates can be sent to the crowdsourcing server.

The crowdsourcing server according to an embodiment may reconfigure a task management process including registering, distributing, assigning, and restoring a result through a spatial task management method for location privacy-aware crowdsourcing.

In addition, grid-based spatial indexes can be used at the heart of privacyware crowdsourcing servers for fast insertion. In this case, it is assumed that the hacker can collect a data package between the participant and the server.

In addition, the participant may transmit the sensing result including the location data and the time data to the crowdsourcing server. Therefore, embodiments consider intercepting data package attacks. Therefore, to protect the participant's location data, we use an index-based encryption method to convert the actual location data into encrypted data, and when inserting the encrypted location data into the crowdsourcing server, we use a grid-based index to reduce the large insertion cost. Can be reduced.

4 is a block diagram illustrating a configuration of a crowdsourcing server according to an embodiment, and FIG. 5 is a flowchart illustrating a method for managing a spatial crowdsourcing task based on privacy-away grids in a crowdsourcing server according to an embodiment. to be.

The crowdsourcing server 100 may include an allocation unit 410, an encryption unit 420, and a receiving unit 430. These components may be representations of different functions performed by a processor in accordance with a control command provided by a program code stored in the crowdsourcing server 100, and the privacy-away grid based space of FIG. The crowdsourcing server 100 may be controlled to perform steps 510 to 530 included in the crowdsourcing task management method. In this case, the components may be implemented to execute instructions according to code of an operating system included in a memory and code of at least one program.

The processor may load program code stored in a file of a program for a privacy-away grid-based spatial crowdsourcing task management method into a memory. For example, when the program is executed in the crowdsourcing server 100, the processor may control the server to load the program code from the file of the program into the memory under the control of the operating system. At this time, each of the processor and the allocating unit 410, the encryption unit 420, and the receiving unit 430 included in the processor executes a command of a corresponding part of the program code loaded in the memory to perform subsequent steps 510 to 530. There may be different functional representations of the processor to execute.

In operation 510, the allocator 410 may allocate a task registered from the requestor to the participant. If a task including a resource type of data is registered from the requester, the allocator 410 may convert the registered task into a task map. The allocator 410 may allocate the sub-map based task to the participants in the converted task map. In this case, the task may refer to a spatial task including at least one or more MBRs, a spatial task, task execution time information, and query information, and the spatial task is a task map represented by a spatial region, task execution time information, and query information. Can be changed to The allocator 410 generates a grid-based index for the registered task from the requestor, divides the task area for the registered task into a plurality of grid cells, and assigns each grid number to the divided plurality of grid cells. Can be. As the task assigned to the participant by the participant is accepted, the allocator 410 may register data related to the participant (eg, participant identification information, participant location data, etc.).

In operation 520, the encryption unit 420 may encrypt the position data included in the result data of the task obtained as the task assigned to the participant is executed. As the task assigned to the participant is executed, the encryption unit 420 performs the task at the position of the moved task, and receives the result data including the participant's identification information, time information, the identification information of the task, and the position data. Can be. The encryption unit 420 may change the position data included in the result data of the performed task into coordinate information of grid cells obtained by dividing the task area. The encryption unit 420 changes the position data included in the result data of the performed task to the relative position data given to the grid cell that divided the task area, and based on the relative position data and the grid number assigned to the grid cell. You can create a grid-based index.

In operation 530, the receiver 430 may receive that the result data including the encrypted location data is inserted into the grid-based index. The receiver 430 may restore the inserted grid-based index.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the devices and components described in the embodiments are, for example, processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable gate arrays (FPGAs). Can be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For convenience of explanation, one processing device may be described as being used, but one of ordinary skill in the art will appreciate that the processing device includes a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as parallel processors.

The software may include a computer program, code, instructions, or a combination of one or more of the above, and configure the processing device to operate as desired, or process it independently or collectively. You can command the device. Software and / or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device in order to be interpreted by or to provide instructions or data to the processing device. It can be embodied in. The software may be distributed over networked computer systems so that they may be stored or executed in a distributed manner. Software and data may be stored on one or more computer readable recording media.

The method according to the embodiment may be embodied in the form of program instructions that can be executed by various computer means and recorded in a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the media may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

Although the embodiments have been described by the limited embodiments and the drawings as described above, various modifications and variations are possible to those skilled in the art from the above description. For example, the described techniques may be performed in a different order than the described method, and / or components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different form than the described method, or other components. Or even if replaced or substituted by equivalents, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are within the scope of the claims that follow.

Claims (6)

In the crowdsourcing task management method performed in the crowdsourcing server,
Assigning a registered task from the requester to the participant as a task including a resource type of data is registered from the requester;
Encrypting location data included in result data of the task obtained as the task assigned to the participant is executed; And
Receiving insertion of the result data including the encrypted position data into a grid-based index
Including,
The task is a spatial task including at least one spatial region including at least one MBR, task execution time information, and query information.
The spatial task may be changed to a task map represented by the spatial region, the task execution time information, and the query information.
The step of assigning a task registered from the requester to the participant may include generating a grid-based index for the task registered from the requester, dividing a task area for the registered task into a plurality of grid cells, Assigning each grid number to one grid cell
Including,
Encrypting the location data included in the result data of the task obtained as the task assigned to the participant is executed,
As the task assigned to the participant is executed, the task is performed at the position of the moved task, and the result data including the participant's identification information, time information, the identification information of the task, and the position data is received, and the performed Change the position data included in the result data for the task to the coordinate information of the grid cell that partitioned the task area, and assign the position data included in the result data for the performed task to the grid cell partitioned the task area Changing the relative position data and generating a grid-based index based on the relative position data and the grid number assigned to the grid cell.
Crowdsourcing task management method comprising a. The method of claim 1,
Assigning a registered task to the participant from the requester,
Converting the registered task into a task map; And
Assigning the converted task map to the participant as a submap-based task;
Crowdsourcing task management method comprising a. delete The method of claim 1,
Assigning a registered task to the participant from the requester,
Registering data associated with the participant as the task assigned to the participant is accepted by the participant;
Crowdsourcing task management method comprising a.
delete delete

Images