CN113163019B - Internet of things privacy protection service discovery system based on SDN and edge computing - Google Patents

Internet of things privacy protection service discovery system based on SDN and edge computing Download PDF

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CN113163019B
CN113163019B CN202110588548.2A CN202110588548A CN113163019B CN 113163019 B CN113163019 B CN 113163019B CN 202110588548 A CN202110588548 A CN 202110588548A CN 113163019 B CN113163019 B CN 113163019B
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context
tree
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internet
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CN113163019A (en
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周潘
周天翔
徐子川
付才
丁晓锋
吴静
胡钰林
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Huazhong University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services

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Abstract

The invention relates to an Internet of things privacy protection service discovery system based on SDN and edge computing, which comprises: the SDN controller is used for controlling each EN to serve as a cooperative learner to cache and process information in the MEC network; the context space model carries out context space partition on the preprocessed context records so as to mine and obtain similar context records; the service space model of the Internet of things covers the total service space through a service history tree and a service discovery tree, the service history tree and the service discovery tree are infinite binary trees, and each tree node corresponds to a service cluster in the total service space; the service history tree stores all historical service records of the Internet of things and marks the partition state of the total service space; the service discovery tree is constructed and refreshed according to the service history tree and the context space and is used for discovering an Internet of things service cluster suitable for the current user so as to select the optimal personalized service and recommend the optimal personalized service to the user; a balance is struck between accuracy of service discovery and the level of privacy protection.

Description

Internet of things privacy protection service discovery system based on SDN and edge computing
Technical Field
The invention relates to the technical field of internet of things, in particular to an internet of things privacy protection service discovery system based on SDN and edge computing.
Background
In the scene of the internet of things, a plurality of heterogeneous entities and intelligent devices are connected with each other under the support of radio frequency identification, wireless sensor networks and other technologies, and a large amount of data related to various physical environments are transmitted. With the help of ubiquitous deployment devices, internet of things services and applications are growing rapidly, covering almost every aspect of our lives (e.g., healthcare, smart home, industry, etc.). Over time, as smart devices continue to grow rapidly, more and more services will be provided to users.
The contextual information of the end user detected by the ambient sensors plays a crucial role in providing personal services. The service discovery system is an important component of an ecosystem of the Internet of things, and finds corresponding services from candidate services by using the context of a user instead of searching a plurality of services randomly. The service discovery can be applied to various scenes such as intelligent home automation, industrial automation and traffic, and the life of people is improved.
However, the highly heterogeneous nature of different users and the ever expanding number of services offered by many service providers in the internet of things greatly impact real-time services. To better meet the needs of individuals, the context of users (e.g., location, time, etc.) is widely used in the context of the internet of things, which raises privacy concerns. Also, many users have several different service requirements in each round, and therefore the service selection should be combined.
Disclosure of Invention
The invention provides an Internet of things privacy protection service discovery system based on SDN and edge calculation, aiming at the technical problems in the prior art, and solves the problems in the prior art.
The technical scheme for solving the technical problems is as follows: an internet of things privacy protection service discovery system based on SDN and edge computing, comprising: EN, SDN controller and service model;
the EN and the SDN controller are deployed in an MEC network; the SDN controller is used for controlling each EN to serve as a cooperative learner to cache and process information in the MEC network;
the service model comprises a context space model and an internet of things service space model;
the context space model carries out context space partition on the context records after preprocessing so as to mine and obtain similar context records;
the service space model of the Internet of things covers the total service space through a service history tree and a service discovery tree, the service history tree and the service discovery tree are infinite binary trees, and each tree node corresponds to a service cluster in the total service space; the service history tree stores all historical service records of the Internet of things and marks the partition state of the total service space; and the service discovery tree is constructed and refreshed according to the service history tree and the context space and is used for discovering an Internet of things service cluster suitable for the current user so as to select the optimal personalized service and recommend the optimal personalized service to the user.
The invention has the beneficial effects that: the invention provides a context-aware online algorithm supporting MEC, which is used for service discovery and supporting large-scale service in IoT, and numerical results show that compared with other context-aware online algorithms, the method has good performance and balances the accuracy of service discovery and the privacy protection level.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the process of the service model selecting the best personalized service to recommend to the user comprises the following steps:
extracting service vectors into the service space of the Internet of things until a user registers new Internet of things service to EN a in the MEC network
Figure DEST_PATH_IMAGE001
To EN a, the current context vector of the user is received
Figure 100002_DEST_PATH_IMAGE002
And carrying out pretreatment in EN a at the t round; wherein EN a represents the a-th EN, the superscript a represents the sequence number of the EN, and the subscript t represents the t-th round;
the context space model is in context space
Figure DEST_PATH_IMAGE003
Finding a context subspace of the service record to obtain enough service records of the Internet of things for reference;
setting a reward estimated value B value representing tree nodes (h, i), and selecting an optimal Internet of things cluster corresponding to the highest B value
Figure DEST_PATH_IMAGE004
；
For the user
Figure 238568DEST_PATH_IMAGE001
Selecting an optimal Internet of things cluster
Figure DEST_PATH_IMAGE005
Set of internet of things services in
Figure DEST_PATH_IMAGE006
In (1)
Figure DEST_PATH_IMAGE007
N is the total number of the Internet of things services in the set of the Internet of things services;
if the number of services selected by the current user
Figure DEST_PATH_IMAGE008
Not more than N, and
Figure DEST_PATH_IMAGE009
is that
Figure DEST_PATH_IMAGE010
Is then in
Figure 264030DEST_PATH_IMAGE010
Lirandon recommendation
Figure DEST_PATH_IMAGE011
Providing the service to the user; otherwise, recommend N services to the user and ask for help from neighboring ENs to recommend others
Figure 106084DEST_PATH_IMAGE011
-N services;
and stopping the whole recommendation process after the user gives feedback.
Further, the process of obtaining similar context records by the context space model comprises:
mapping each of the user's contexts to one
Figure DEST_PATH_IMAGE012
Dimensional context space
Figure 766873DEST_PATH_IMAGE003
(ii) a In the t-th round, the user has a context vector
Figure 174720DEST_PATH_IMAGE002
The context vector is encoded
Figure DEST_PATH_IMAGE013
Sending to the context space model;
in each round, acquiring a valid record by mining the context information of the user, and taking the valid record as a reference for selecting the service type;
for the context space
Figure 793920DEST_PATH_IMAGE003
Partitioning into a plurality of context subspaces, and ensuring that the maximum number of context records in each space does not exceed a set threshold; finding the context vector
Figure 388850DEST_PATH_IMAGE013
The subspaces to which the context information belongs, and the appropriate context space containing the context-related information is obtained to obtain similar context records.
Further, the calculation formula of the reward estimation value B is as follows:
Figure DEST_PATH_IMAGE014
；
wherein subscript h, i represents tree node (h, i);
Figure DEST_PATH_IMAGE015
the average reward of experience is shown, and gamma is a parameter for balancing exploration and development;
Figure DEST_PATH_IMAGE016
the expression of the Liphoz constant is shown,
Figure DEST_PATH_IMAGE017
representing a context space
Figure DEST_PATH_IMAGE018
The maximum distance of (a) is,
Figure DEST_PATH_IMAGE019
represents the upper limit of the diameter of the subset region of tree nodes (h, i), infinity represents infinity;
Figure DEST_PATH_IMAGE020
；
Figure DEST_PATH_IMAGE021
；
Figure DEST_PATH_IMAGE022
representing the number of times tree nodes (h, i) are selected at ena until round t,
Figure DEST_PATH_IMAGE023
representing the set of ena and its single-hop neighbors,
Figure DEST_PATH_IMAGE025
to indicate the function, n is the total number of rounds.
Further, the empirical average reward of the tree nodes (h, i) is:
Figure DEST_PATH_IMAGE027
；
wherein,
Figure DEST_PATH_IMAGE028
is as follows.
Further, the service history tree is used in the service space model of the internet of things
Figure DEST_PATH_IMAGE029
And context space
Figure 414181DEST_PATH_IMAGE013
Refreshing the service discovery tree
Figure DEST_PATH_IMAGE030
And updating the B value, including:
for the
Figure DEST_PATH_IMAGE031
To judge the existence
Figure DEST_PATH_IMAGE032
Not exceeding a set threshold
Figure DEST_PATH_IMAGE033
EN a seeks help from its neighboring hop EN, for EN a
Figure DEST_PATH_IMAGE034
All ENs in time, the empirical average reward is calculated as:
Figure DEST_PATH_IMAGE035
；
computing and updating the service discovery tree
Figure DEST_PATH_IMAGE036
Average empirical consideration of
Figure DEST_PATH_IMAGE037
Then, the place is calculated and updatedThe value of B is as follows
Figure DEST_PATH_IMAGE038
Adding to the service discovery tree
Figure 597907DEST_PATH_IMAGE036
In (1).
Further, the service model also includes a local differential privacy mechanism
Figure DEST_PATH_IMAGE039
To the user
Figure 653587DEST_PATH_IMAGE001
Initial context vector of
Figure 735813DEST_PATH_IMAGE013
And carrying out context random data perturbation processing, and calculating the influence of the perturbation processing on service discovery through a loss function.
Further, the local differential privacy mechanism
Figure 66300DEST_PATH_IMAGE039
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE040
；
wherein,
Figure DEST_PATH_IMAGE041
as a context vector
Figure 855265DEST_PATH_IMAGE013
Via the local differential privacy mechanism
Figure 878584DEST_PATH_IMAGE039
Perturbing the set of post-operation mappings;
Figure DEST_PATH_IMAGE042
，
Figure DEST_PATH_IMAGE043
，
Figure DEST_PATH_IMAGE044
representing a privacy factor;
the loss function is:
Figure DEST_PATH_IMAGE045
。
further, after the service model selects the best personalized service to recommend to the user, the discovery system calculates a total reward value according to the server side reward and the user side reward:
Figure DEST_PATH_IMAGE046
；
Figure DEST_PATH_IMAGE047
and
Figure DEST_PATH_IMAGE048
is a parameter, and
Figure DEST_PATH_IMAGE049
；
Figure DEST_PATH_IMAGE050
server-side rewards obtained from various ENs monitored by the SDN controller,
Figure DEST_PATH_IMAGE051
and rewarding the user terminal determined according to the execution time, the response time and the reliability of the user.
Further, the discovery system performs offline update according to the completed service record:
in the service space model of the Internet of things, the service history tree is used for the service
Figure 974671DEST_PATH_IMAGE029
Each leaf tree node in (1)
Figure DEST_PATH_IMAGE052
If, if
Figure DEST_PATH_IMAGE053
(ii) a Updating the service history tree:
Figure DEST_PATH_IMAGE054
；
updating the B value:
Figure DEST_PATH_IMAGE055
；
in the context space model, if
Figure DEST_PATH_IMAGE056
Then further dividing the context space
Figure 30221DEST_PATH_IMAGE003
；
Wherein,
Figure DEST_PATH_IMAGE057
representing context vectors
Figure 736009DEST_PATH_IMAGE013
The number of contexts in (a) and (b),
Figure DEST_PATH_IMAGE058
and
Figure DEST_PATH_IMAGE059
indicating that a threshold is set.
The beneficial effect of adopting the further scheme is that: only a single arm is considered to be selected in a round against the conventional CMAB algorithm, but many users have multiple requirements in a round and are therefore not applicable. The invention provides a context-aware online algorithm for local differential privacy and MEC support, which can process dynamic complex context problems, carry out balance between development and exploration after selecting proper arm, and select a group of arms by utilizing combined contextual multi-arm basis (CC-MAB) to meet different service requirements of each round of users. And the system can continuously learn the rewards of arms on the web, as the user context arrives, to maximize the total rewards.
Sensitive information of a user is protected on a user level instead of a service discovery system level by using a Local Differential Privacy (LDP) mechanism, random noise is introduced to interfere individual context by combining LDP and CC-MAB, the privacy of the user is ensured, and the balance between service discovery precision and local privacy/personal information utility is realized. The accuracy of the system is measured using the regret concept in multi-arm bandit, which is defined as the gap between the optimal service and the reward of the actually selected service, the sub-linear regret means that our method converges to the optimal service discovery strategy. The L2 loss was introduced as a practical measure to evaluate privacy loss and data utility during random perturbations. We have demonstrated theoretically that our algorithm can achieve a sub-linear regret, ensuring that promising local differential privacy is provided for individual ENs and users, but without significantly impacting the information utility.
Drawings
Fig. 1 is an interaction diagram of an internet of things privacy protection service discovery system based on SDN and edge computing according to an embodiment of the present invention;
fig. 2 is a schematic diagram of context space partitioning according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
MEC (Mobile Edge Computing) is a technology based on 5G evolution architecture and integrates Mobile access networks with internet services deeply. The method has lower delay time so as to relieve network congestion and improve the experience quality of users. Data caching and processing may be performed on individual ENs (Edge Node services), where services may be deployed to end users with real-time responses. In addition, in order to realize a distributed ubiquitous computing scenario with high complexity and logic control on each EN, the Network complexity may be managed by using an SDN (Software Defined Network) that hides all internal details in the MEC Network, the control and the data plane are separated, and the programmable control is realized, so that the SDN controller can obtain a complete view and centralized control of the entire Network, and is more flexible and reliable. Furthermore, it can be deployed on eligible ENs in the MEC network, which is considered a promising solution to efficiently provide computing resources and coordinate the MEC network. Therefore, it is beneficial to cooperate the MEC supporting SDN with a service discovery system to provide real-time, low-latency services to users.
An internet of things privacy protection service discovery system based on SDN and edge computing, the discovery system comprising: EN, SDN controller, and service model.
The EN and the SDN controller are deployed in an MEC network; the SDN controller is used for controlling each EN to serve as a cooperative learner to cache and process information in the MEC network.
The service model comprises a context space model and an internet of things service space model.
And the context space model carries out context space partition on the preprocessed context information so as to mine and obtain similar context records.
The service space model of the Internet of things covers the total service space through a service history tree and a service discovery tree, wherein the service history tree and the service discovery tree are infinite binary trees; the service history tree stores all historical service records of the Internet of things and marks the partition state of the total service space; and the service discovery tree is constructed and refreshed according to the service history tree and the context space and is used for discovering an Internet of things service cluster suitable for the current user so as to select the optimal personalized service and recommend the optimal personalized service to the user.
The service history tree stores all historical service records of the Internet of things up to the t round, and the tree nodes in the service history tree can be gradually divided along with the increase of the history records. The service discovery tree may be refreshed based on a similar context space selected for obtaining a similar history and containing useful tree nodes in the service history tree. In the whole process, only the tree nodes containing the context related records of the service discovery tree in the service history tree are considered, and the whole service history tree is not considered. A particular internet of things service cluster is selected and the partitioning will be finer as the depth of the tree increases.
Example 1
Embodiment 1 provided in the present invention is an embodiment of a system for discovering internet of things privacy protection service based on SDN and edge computing, and as shown in fig. 1, an interaction diagram of the system for discovering internet of things privacy protection service based on SDN and edge computing provided in the embodiment of the present invention is shown, where the discovery system includes: EN, SDN controller, and service model.
The EN and the SDN controller are deployed in an MEC network; the SDN controller is used for controlling each EN to serve as a cooperative learner to cache and process information in the MEC network.
The service model comprises a context space model and an internet of things service space model.
And the context space model carries out context space partition on the preprocessed context information so as to mine and obtain similar context records.
The service space model of the Internet of things covers the total service space through a service history tree and a service discovery tree, wherein the service history tree and the service discovery tree are infinite binary trees; the service history tree stores all historical service records of the Internet of things and marks the partition state of the total service space; and the service discovery tree is constructed and refreshed according to the service history tree and the context space and is used for discovering an Internet of things service cluster suitable for the current user so as to select the optimal personalized service and recommend the optimal personalized service to the user.
Preferably, the process of the service model selecting the best personalized service recommendation to the user comprises:
step 1, when a new internet of things service is registered with EN a in the MEC network, extracting a service vector into the internet of things service space until a user
Figure 461388DEST_PATH_IMAGE001
To EN a, the current context vector of the user is received
Figure 659151DEST_PATH_IMAGE002
And carrying out pretreatment in EN a at the t round; EN a denotes the a-th ENE, the superscript a denotes the EN sequence number, and the subscript t denotes the t-th round.
Step 2, the context space model is in the context space
Figure 65862DEST_PATH_IMAGE003
Finds its context subspace to obtain enough service records of the internet of things for reference.
Specifically, the process of obtaining similar context records by the context space model includes:
mapping each of said user's contexts (which may be hobbies, location, time and duration, etc.) to one
Figure 32681DEST_PATH_IMAGE012
Dimensional context space
Figure 132224DEST_PATH_IMAGE003
(ii) a Mapping each of the user's contexts to one
Figure 676338DEST_PATH_IMAGE012
Dimensional context space
Figure 27685DEST_PATH_IMAGE003
(ii) a In the t-th round, the user has a context vector
Figure 973644DEST_PATH_IMAGE002
The context vector is encoded
Figure 385034DEST_PATH_IMAGE013
Sending to the context space model; context vector
Figure 150864DEST_PATH_IMAGE013
By
Figure 40323DEST_PATH_IMAGE012
And (5) modeling dimensional vectors.
FIG. 2 is a diagram illustrating a context space partition according to an embodiment of the present invention, where FIG. 2 is a diagram illustrating a context space partition
Figure DEST_PATH_IMAGE060
And
Figure DEST_PATH_IMAGE061
respectively represent
Figure 903106DEST_PATH_IMAGE060
And
Figure 610031DEST_PATH_IMAGE061
the time of the wheel is such that,
Figure DEST_PATH_IMAGE062
、
Figure DEST_PATH_IMAGE063
and
Figure DEST_PATH_IMAGE064
respectively, Lc represents the Liphoz constant.
Normalizing the range of each dimension of a context record to [0,1 ]]Thus C is
Figure DEST_PATH_IMAGE065
A context space. As can be seen in connection with the embodiment shown in FIG. 2, the three-dimensional context space contains users
Figure DEST_PATH_IMAGE066
Service record of
Figure DEST_PATH_IMAGE067
Up to
Figure 987791DEST_PATH_IMAGE060
Wheels, where dimensions may be age, monthly salary, and man-hours. For example,
Figure DEST_PATH_IMAGE068
=0.20 means that the user is
Figure 133471DEST_PATH_IMAGE066
Is about 20 years old, and the maximum age is 100 years old, when
Figure DEST_PATH_IMAGE069
When =0.89, he/she belongs to a high salary class because the threshold for the average payroll of 1200$ per month is 0.5. If it is not
Figure DEST_PATH_IMAGE070
=0.26, maximum value 24, this indicates the user
Figure 460547DEST_PATH_IMAGE066
Work was done for about 6.2 hours per day.
In each round, a valid record is obtained by mining the context information of the user, and the valid record is used as a reference for selecting the service type.
For the context space
Figure 338373DEST_PATH_IMAGE003
Partitioning into a plurality of subspaces, and ensuring that the maximum number of context records in each space does not exceed a set threshold; finding the context vector
Figure 813217DEST_PATH_IMAGE013
The subspaces to which the context information belongs are obtained into an appropriate context space containing the context-related information so as to obtain similar context records for accurate selection.
For example, for the above-mentioned users
Figure 903532DEST_PATH_IMAGE066
Similar user records (high salary, young and easy to work) may be consulted.
Only the entire context space in the case of few history records is considered initially. As records increase, the context space can be divided more finely. T rounds since the recording is more complete
Figure DEST_PATH_IMAGE071
The time becomes shorter. At the time of
Figure 881853DEST_PATH_IMAGE061
，
Figure 665001DEST_PATH_IMAGE071
Only a small subspace is covered, which contains more contextual users. Setting a threshold value
Figure DEST_PATH_IMAGE072
Limiting the maximum number in each space when the number of records is greater than or equal to a threshold
Figure 373280DEST_PATH_IMAGE072
The space is further partitioned for accurate assessment. The context space only needs to be found
Figure 408232DEST_PATH_IMAGE071
The appropriate context space containing the context-related information can be obtained from the subspaces to which it belongs. If subspace
Figure 37797DEST_PATH_IMAGE071
Is greater than a threshold
Figure 991846DEST_PATH_IMAGE072
Then, then
Figure 441282DEST_PATH_IMAGE071
Will be partitioned and the context space will be updated. By using
Figure DEST_PATH_IMAGE073
To represent
Figure 607821DEST_PATH_IMAGE071
The maximum distance of (c).
Step 3, setting the reward estimated value B of the tree node (h, i), and selecting the optimal Internet of things cluster corresponding to the highest B value
Figure 967258DEST_PATH_IMAGE004
. Each tree node corresponds to a service cluster in the total service space,
Figure DEST_PATH_IMAGE074
。
preferably, the calculation formula of the reward estimate value B is:
Figure 685685DEST_PATH_IMAGE014
。
wherein, the superscript a represents the sequence number of EN, the subscript t represents the t-th round, and the subscript h, i represents the tree node (h, i);
Figure 763362DEST_PATH_IMAGE015
the average reward of experience is shown, and gamma is a parameter for balancing exploration and development;
Figure 530330DEST_PATH_IMAGE016
the expression of the Liphoz constant is shown,
Figure 744273DEST_PATH_IMAGE017
to represent
Figure 305705DEST_PATH_IMAGE018
The maximum distance of (a) is,
Figure 870678DEST_PATH_IMAGE019
represents the upper bound of the diameter of the subset region of tree nodes (h, i), infinity representing infinity.
Figure DEST_PATH_IMAGE075
Is that
Figure 503654DEST_PATH_IMAGE018
The uncertainty of the context.
Exist of
Figure DEST_PATH_IMAGE076
And 0 ≦ b ≦ 1, so for any tree node (h, i):
Figure DEST_PATH_IMAGE077
，
Figure DEST_PATH_IMAGE078
Figure DEST_PATH_IMAGE079
indicating the diameter of the subset area.
The B value represents the reward estimate for the tree node (h, i), used as the basis for service discovery. The first item is based on the evaluation of service cluster performance previously recorded, i.e. the exploitation of past experience. The second term is the uncertainty of the cluster feedback, indicating the exploration level of the cluster. The last term represents the uncertainty of the tree node size.
The empirical average reward of the tree nodes (h, i) is:
Figure 555792DEST_PATH_IMAGE027
。
wherein,
Figure 163491DEST_PATH_IMAGE028
is awarded for the t-th round.
Figure 340395DEST_PATH_IMAGE020
。
Figure DEST_PATH_IMAGE080
。
Figure 386848DEST_PATH_IMAGE022
Representing the number of times tree nodes (h, i) are selected at ena until round t,
Figure 840963DEST_PATH_IMAGE023
representing the set of ena and its single-hop neighbors,
Figure 478618DEST_PATH_IMAGE025
to indicate the function, n is the total number of rounds.
Further, the service history tree is used in the service space model of the internet of things
Figure 142817DEST_PATH_IMAGE029
And context space
Figure 665066DEST_PATH_IMAGE003
Refreshing the service discovery tree
Figure 832742DEST_PATH_IMAGE030
And updating the B value, including:
for the
Figure 782243DEST_PATH_IMAGE031
To judge the existence
Figure 668160DEST_PATH_IMAGE032
Not exceeding a set threshold
Figure 994099DEST_PATH_IMAGE033
EN a, then considering that EN a is an inexperienced node, needs to learn from other nodes and let EN a seek help from its neighboring EN by one hop, for
Figure 281861DEST_PATH_IMAGE034
All ENs in time, the empirical average reward is calculated as:
Figure 261318DEST_PATH_IMAGE035
。
computing and updating the service discovery tree
Figure 775476DEST_PATH_IMAGE036
Average empirical consideration of
Figure 764160DEST_PATH_IMAGE037
Then, the B value is calculated and updated, and
Figure 781795DEST_PATH_IMAGE038
adding to the service discovery tree
Figure 197733DEST_PATH_IMAGE036
In (1).
The discovery system updates only the B-values of tree nodes containing relevant records and adds them to the service discovery tree, thereby reducing the number of candidates for service clusters and the likelihood of selecting to unrelated tree nodes.
Step 4, the user is provided with
Figure 933608DEST_PATH_IMAGE001
Selecting an optimal Internet of things cluster
Figure DEST_PATH_IMAGE081
Set of internet of things services in
Figure 194825DEST_PATH_IMAGE006
In (1)
Figure 926020DEST_PATH_IMAGE007
And N is the total number of the Internet of things services in the set of the Internet of things services.
Step 5, if the number of the services selected by the current user
Figure 778439DEST_PATH_IMAGE008
Not more than N, and
Figure 1610DEST_PATH_IMAGE009
is that
Figure 66518DEST_PATH_IMAGE010
Is then in
Figure 58744DEST_PATH_IMAGE010
Lirandon recommendation
Figure 82064DEST_PATH_IMAGE011
Providing the service to the user; otherwise, recommend N services to the user and ask for help from neighboring ENs to recommend others
Figure 386006DEST_PATH_IMAGE011
-N services.
And 6, stopping the whole recommendation process after the user gives feedback.
The service model also includes a local differential privacy mechanism
Figure 395551DEST_PATH_IMAGE039
And step 2 is followed by: for the user
Figure 366918DEST_PATH_IMAGE001
Initial context vector of
Figure 29980DEST_PATH_IMAGE013
And carrying out context random data perturbation processing, and calculating the influence of the perturbation processing on better service discovery through a loss function.
The local differential privacy mechanism
Figure 227743DEST_PATH_IMAGE039
The calculation formula of (2) is as follows:
Figure 646173DEST_PATH_IMAGE040
。
wherein,
Figure 612992DEST_PATH_IMAGE041
as a context vector
Figure 712535DEST_PATH_IMAGE013
Via the local differential privacy mechanism
Figure 256649DEST_PATH_IMAGE039
Perturbing the set of post-operation mappings;
Figure 607996DEST_PATH_IMAGE042
，
Figure 553955DEST_PATH_IMAGE043
，
Figure 965345DEST_PATH_IMAGE044
representing a privacy factor.
The loss function is:
Figure 731175DEST_PATH_IMAGE045
。
further, after service provisioning, the user explicitly or implicitly provides feedback to the system (user-side rewards if the service is selected by the user, the service-side rewards may be evaluated by data monitored by the MEC network, and if not, the latest history may be utilized.
After the service system selects the optimal personalized service to recommend to the user, the discovery system calculates a total reward value according to the reward of the service end and the reward of the user end:
Figure 620634DEST_PATH_IMAGE046
。
Figure 421100DEST_PATH_IMAGE047
and
Figure 862445DEST_PATH_IMAGE048
is a parameter, and
Figure 256517DEST_PATH_IMAGE049
。
Figure 808722DEST_PATH_IMAGE050
server-side rewards obtained from various ENs monitored by the SDN controller,
Figure 604639DEST_PATH_IMAGE051
the user terminal is awarded according to the execution time, the response time, the reliability and the like of the user.
Further, the discovery system also performs offline updating according to the completed service record:
in the service space model of the Internet of things, the service history tree is used for the service
Figure 482465DEST_PATH_IMAGE029
Each leaf tree node in (1)
Figure 363834DEST_PATH_IMAGE052
If, if
Figure 454149DEST_PATH_IMAGE053
(ii) a This indicates that leaf nodes have been selected a sufficient number of times that the tree should be expanded to obtain more precise regions and finer divisions, the service history tree is updated:
Figure 229207DEST_PATH_IMAGE054
。
updating the B value:
Figure 153301DEST_PATH_IMAGE055
。
in the context space model, if
Figure 646599DEST_PATH_IMAGE056
Then further dividing the context space
Figure 415972DEST_PATH_IMAGE003
。
Wherein,
Figure 45537DEST_PATH_IMAGE057
representing context vectors
Figure 140532DEST_PATH_IMAGE013
The number of contexts in (a) and (b),
Figure 855547DEST_PATH_IMAGE058
and
Figure 553244DEST_PATH_IMAGE059
indicating that a threshold is set.
The method specifically comprises the following steps:
Figure DEST_PATH_IMAGE082
，
Figure DEST_PATH_IMAGE083
，
Figure DEST_PATH_IMAGE084
and
Figure DEST_PATH_IMAGE085
to set the parameters.
The invention provides a context-aware online algorithm for local differential privacy and MEC support, which realizes sub-linear regret boundary and local differential privacy under the condition of not influencing the utility of context information and balances the accuracy of service discovery and the privacy protection level.
Only a single arm is considered to be selected in a round against the conventional CMAB algorithm, but many users have multiple requirements in a round and are therefore not applicable. The invention provides a context-aware online algorithm for local differential privacy and MEC support, which can process dynamic complex context problems, carry out balance between development and exploration after selecting proper arm, and select a group of arms by utilizing combined contextual multi-arm basis (CC-MAB) to meet different service requirements of each round of users. And the system can continuously learn the rewards of arms on the web, as the user context arrives, to maximize the total rewards.
Sensitive information of a user is protected on a user level instead of a service discovery system level by using a Local Differential Privacy (LDP) mechanism, random noise is introduced to interfere individual context by combining LDP and CC-MAB, the privacy of the user is ensured, and the balance between service discovery precision and local privacy/personal information utility is realized. The accuracy of the system is measured using the regret concept in multi-arm bandit, which is defined as the gap between the optimal service and the reward [35] of the actually selected service, the sub-linear regret means that our method converges to the optimal service discovery strategy. The L2 loss was introduced as a practical measure to evaluate privacy loss and data utility during random perturbations. We have demonstrated theoretically that our algorithm can achieve a sub-linear regret, ensuring that promising local differential privacy is provided for individual ENs and users, but without significantly impacting the information utility.
The invention provides a new context and combination bandit-based local differential privacy online learning method, which is used for service discovery in the Internet of things through an SDN. It guarantees that the sub-linear regret boundary indicates that the algorithm converges to the optimal strategy. Personalized service selection is possible because we take into account the end user's context and process the context information with adaptive context space partitioning. The performance of the IoT service is measured through the user side rewards and the service side rewards, so that the accuracy of the whole service discovery is improved. Under the cooperation of each EN and dynamic complex conditions in the Internet of things, the method supports a large data set which is continuously increased in an MEC scene. The privacy of the user is protected with local differential privacy while a balance is struck between the privacy protection level and the information utility.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (8)

1. An internet of things privacy protection service discovery system based on SDN and edge computing, the discovery system comprising: EN, SDN controller and service model;
the EN and the SDN controller are deployed in an MEC network; the SDN controller is used for controlling each EN to serve as a cooperative learner to cache and process information in the MEC network;
the service model comprises a context space model and an internet of things service space model;
the context space model carries out context space partition on the context records after preprocessing so as to mine and obtain similar context records;
the service space model of the Internet of things covers the total service space through a service history tree and a service discovery tree, the service history tree and the service discovery tree are infinite binary trees, and each tree node corresponds to a service cluster in the total service space; the service history tree stores all historical service records of the Internet of things and marks the partition state of the total service space; the service discovery tree is constructed and refreshed according to the service history tree and the context space and is used for discovering an Internet of things service cluster suitable for the current user so as to select the optimal personalized service and recommend the optimal personalized service to the user;
the service model also includes a local differential privacy mechanism
Figure 545676DEST_PATH_IMAGE001
To the user
Figure 684533DEST_PATH_IMAGE002
Initial context vector of
Figure 267961DEST_PATH_IMAGE003
The context random data perturbation processing is carried out,
Figure 314415DEST_PATH_IMAGE001
representing injected context vectors
Figure 299688DEST_PATH_IMAGE003
The influence of the disturbance processing on the service discovery is calculated through a loss function;
the local differential privacy mechanism
Figure 609447DEST_PATH_IMAGE001
The calculation formula of (2) is as follows:
Figure 617854DEST_PATH_IMAGE004
；
wherein,
Figure 671261DEST_PATH_IMAGE005
as a context vector
Figure 511041DEST_PATH_IMAGE006
Via the local differential privacy mechanism
Figure 991701DEST_PATH_IMAGE007
Perturbing the set of post-operation mappings;
Figure 861305DEST_PATH_IMAGE008
，
Figure 718403DEST_PATH_IMAGE009
，
Figure 412689DEST_PATH_IMAGE010
representing a privacy factor;
Figure 860988DEST_PATH_IMAGE011
representing a context space
Figure 906305DEST_PATH_IMAGE012
Dimension;
the loss function is:
Figure 301514DEST_PATH_IMAGE013
。
2. the discovery system of claim 1 wherein said service model selects the best personalized service to recommend to said user comprises:
extracting service vectors into the service space of the Internet of things until a user registers new Internet of things service to EN a in the MEC network
Figure 928935DEST_PATH_IMAGE014
To EN a, the current context vector of the user is received
Figure 16977DEST_PATH_IMAGE015
And carrying out pretreatment in EN a at the t round; wherein EN a represents the a-th EN, the superscript a represents the sequence number of the EN, and the subscript t represents the t-th round;
the context space model is in context space
Figure 284010DEST_PATH_IMAGE012
Finding a context subspace of the service record to obtain enough service records of the Internet of things for reference;
setting a reward estimated value B value representing tree nodes (h, i), and selecting an optimal Internet of things cluster corresponding to the highest B value
Figure 482911DEST_PATH_IMAGE016
；
For the user
Figure 322428DEST_PATH_IMAGE014
Selecting an optimal Internet of things cluster
Figure 581371DEST_PATH_IMAGE017
Set of internet of things services in
Figure 335701DEST_PATH_IMAGE018
In (1)
Figure 869450DEST_PATH_IMAGE019
N is the total number of the Internet of things services in the set of the Internet of things services;
if the number of services selected by the current user
Figure 392836DEST_PATH_IMAGE020
Not more than N, and
Figure 822680DEST_PATH_IMAGE021
is that
Figure 877355DEST_PATH_IMAGE022
Is then in
Figure 418057DEST_PATH_IMAGE022
Lirandon recommendation
Figure 61528DEST_PATH_IMAGE023
Providing the service to the user; otherwise, recommend N services to the user and ask for help from neighboring ENs to recommend others
Figure 396695DEST_PATH_IMAGE023
-N services;
and stopping the whole recommendation process after the user gives feedback.
3. The discovery system of claim 1 wherein said process of obtaining similar context records by said context space model comprises:
mapping each of the user's contexts to one
Figure 922354DEST_PATH_IMAGE011
Dimensional context space
Figure 1168DEST_PATH_IMAGE024
(ii) a In the t-th round, the user has a context vector
Figure 499146DEST_PATH_IMAGE025
The context vector is encoded
Figure 942897DEST_PATH_IMAGE006
Sending to the context space model;
in each round, acquiring a valid record by mining the context information of the user, and taking the valid record as a reference for selecting the service type;
for the context space
Figure 159114DEST_PATH_IMAGE024
Partitioning into a plurality of context subspaces, and ensuring that the maximum number of context records in each space does not exceed a set threshold; finding the context vector
Figure 41620DEST_PATH_IMAGE006
The subspaces to which the context information belongs, and the appropriate context space containing the context-related information is obtained to obtain similar context records.
4. The discovery system of claim 2 wherein said reward estimate value, B, is calculated by the formula:
Figure 971267DEST_PATH_IMAGE026
；
wherein subscript h, i represents tree node (h, i);
Figure 913816DEST_PATH_IMAGE027
the average reward of experience is shown, and gamma is a parameter for balancing exploration and development;
Figure 351750DEST_PATH_IMAGE028
the expression of the Liphoz constant is shown,
Figure 772367DEST_PATH_IMAGE029
representing a context space
Figure 41675DEST_PATH_IMAGE030
The maximum distance of (a) is,
Figure 889545DEST_PATH_IMAGE031
represents the upper limit of the diameter of the subset region of tree nodes (h, i), infinity represents infinity;
Figure 814776DEST_PATH_IMAGE032
；
Figure 117712DEST_PATH_IMAGE033
；
Figure 444788DEST_PATH_IMAGE034
representing the number of times tree nodes (h, i) are selected at ena until round t,
Figure 729139DEST_PATH_IMAGE035
representing the set of ena and its single-hop neighbors,
Figure DEST_PATH_IMAGE002
to indicate the function, n is the total number of rounds.
5. The discovery system according to claim 4, wherein the empirical average reward of a tree node (h, i) is:
Figure 340304DEST_PATH_IMAGE038
；
wherein,
Figure 521886DEST_PATH_IMAGE039
is awarded for the t-th round.
6. The discovery system of claim 4, wherein said IOT service space model is based on said service history tree
Figure 977138DEST_PATH_IMAGE040
And context space
Figure 673699DEST_PATH_IMAGE006
Refreshing the service discovery tree
Figure 239810DEST_PATH_IMAGE042
And updating the B value, including:
for the
Figure 275899DEST_PATH_IMAGE043
To judge the existence
Figure 980681DEST_PATH_IMAGE044
Not exceeding a set threshold
Figure 367800DEST_PATH_IMAGE045
EN a seeks help from its neighboring hop EN, for EN a
Figure 472022DEST_PATH_IMAGE046
All ENs in time, the empirical average reward is calculated as:
Figure 424934DEST_PATH_IMAGE047
；
computing and updating the service discovery tree
Figure 221989DEST_PATH_IMAGE048
Average empirical consideration of
Figure 830825DEST_PATH_IMAGE049
Then, the B value is calculated and updated, and
Figure 942000DEST_PATH_IMAGE050
adding to the service discovery tree
Figure 952682DEST_PATH_IMAGE048
In (1).
7. The discovery system of claim 1 wherein, after said service model selects the best personalized service to recommend to said user, said discovery system calculates a total reward value based on the server-side reward and the client-side reward:
Figure 920638DEST_PATH_IMAGE051
；
Figure 16770DEST_PATH_IMAGE052
and
Figure 39958DEST_PATH_IMAGE053
is a parameter, and
Figure 170725DEST_PATH_IMAGE054
；
Figure 309583DEST_PATH_IMAGE055
server-side rewards obtained from various ENs monitored by the SDN controller,
Figure 689748DEST_PATH_IMAGE056
according to the execution time of the user,Response time and reliability determination of the user terminal reward.
8. The discovery system of claim 1 wherein said discovery system performs offline updates based on completed business records:
in the service space model of the Internet of things, the service history tree is used for the service
Figure 939464DEST_PATH_IMAGE040
Each leaf tree node in (1)
Figure 924738DEST_PATH_IMAGE057
If, if
Figure 313125DEST_PATH_IMAGE058
(ii) a Updating the service history tree:
Figure 383849DEST_PATH_IMAGE059
；
updating the B value:
Figure 437256DEST_PATH_IMAGE060
；
in the context space model, if
Figure 277036DEST_PATH_IMAGE061
Then further dividing the context space
Figure 193914DEST_PATH_IMAGE024
；
Wherein,
Figure 486355DEST_PATH_IMAGE062
representing context vectors
Figure 343452DEST_PATH_IMAGE006
The number of contexts in (a) and (b),
Figure 834477DEST_PATH_IMAGE063
and
Figure 486038DEST_PATH_IMAGE064
indicating that a threshold is set.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110198302A (en) * 2019-04-26 2019-09-03 华中科技大学 A kind of method for secret protection and system for intelligent electric meter data publication
CN110650487A (en) * 2019-09-27 2020-01-03 常熟理工学院 Internet of things edge computing configuration method based on data privacy protection

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10778412B2 (en) * 2017-12-28 2020-09-15 Intel Corporation Multi-domain convolutional neural network
US11244242B2 (en) * 2018-09-07 2022-02-08 Intel Corporation Technologies for distributing gradient descent computation in a heterogeneous multi-access edge computing (MEC) networks
CN109543094B (en) * 2018-09-29 2021-09-28 东南大学 Privacy protection content recommendation method based on matrix decomposition
CN112035755B (en) * 2020-07-14 2023-04-07 中国科学院信息工程研究所 User-centered personalized recommendation privacy protection method and system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110198302A (en) * 2019-04-26 2019-09-03 华中科技大学 A kind of method for secret protection and system for intelligent electric meter data publication
CN110650487A (en) * 2019-09-27 2020-01-03 常熟理工学院 Internet of things edge computing configuration method based on data privacy protection

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A Differentially Private Unscented Kalman Filter for Streaming Data in IoT;Jun Wang;《IEEE Access》;20180123;第6卷;全文 *
Privacy-Preserving MEC-Enabled Contextual Online Learning via SDN for Service Selection in IoT;Difan Mu;《 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)》;20191207;第I-III节 *
基于边缘辅助连接的位置差分隐私保护的研究;苗秋成;《中国优秀硕士学位论文全文数据库 信息科技辑》;20200115(第01期);全文 *
移动互联服务与隐私保护的研究进展;李晖等;《通信学报》;20141125(第11期);全文 *

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