CN115859331A - Smart city information safety guarantee system - Google Patents
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Abstract
The invention discloses a smart city information safety guarantee system, which belongs to the technical field of information safety guarantee, and can actively judge whether an original data set requested by a data user is matched with a smart city task required by the data user when a smart city is built, actively prompt an abnormal original data set request, strengthen the auditing and management supervision of the data user, and avoid the data user abusing the original data set; according to the invention, by utilizing the characteristics that the original data group to be acquired with a large auditing coefficient has wide application range and high confidentiality value, has more and more complex auditing processes and longer auditing period, when the related work of the original data group to be acquired is processed, the data acquisition party and the data use party corresponding to the original data group to be acquired with a large auditing coefficient are preferentially audited, so that the problem that the auditing flow of the original data group to be acquired is prolonged and the construction progress of a smart city is influenced can be avoided.
Description
Technical Field
The invention belongs to the technical field of information safety guarantee, and particularly relates to an intelligent city information safety guarantee system.
Background
With the advance of urbanization, the introduction of smart cities can improve the utilization efficiency of urban resources, optimize urban management and service and improve the living standard of residents.
In the process of the actual operation, as the quantity of projects related to the smart city construction is large, the related work is more, and social enterprises and research institutions need to be introduced to participate in the actual operation, so that a part of data is required to be opened by government institutions to be used by the social enterprises and the research institutions, and innovation is encouraged according to the opened data so as to create higher value, but the data is leaked, the risk of illegal use and abuse of the data is greatly improved, in addition, for the abuse of the opened data, the auditing of a data user needs to be enhanced, generally, the data with larger confidentiality requirement and wider use range is obtained, the auditing period is longer, the difficulty is higher, and the construction progress of the smart city is delayed.
Disclosure of Invention
The invention aims to provide a smart city information security guarantee system, which solves the problem that in the prior art, when smart city construction is carried out, the released city data is illegally used and has a high risk of abuse.
The purpose of the invention can be realized by the following technical scheme:
a smart city information safety guarantee system, comprising:
the data storage module is used for storing the original data group;
the auditing module is used for auditing a data acquisition party of the original data group to be acquired and a data use party corresponding to the original data group to be acquired;
the working method of the intelligent city information safety guarantee system comprises the following steps:
the method comprises the steps that firstly, when a smart city task is executed, a data set corresponding to the smart city task is obtained, and the data set comprises a plurality of original data sets;
secondly, classifying the smart city tasks, and classifying the smart city tasks meeting the same purpose into the same task group;
in the same task group, acquiring the number n of smart city tasks, acquiring the corresponding occurrence times R of each group of original data in the task group in the smart city tasks, and calculating the correlation coefficient R of each original data group corresponding to the task group according to the formula R = R/n;
thirdly, when a city needs to be constructed, acquiring a smart city task which needs to be carried out, and taking the execution unit as a data user;
the data user side initiates an original data acquisition request to the data palm control side according to the smart city task required to be performed, wherein the original data acquisition request comprises an original data set required by executing the corresponding smart city task;
acquiring a correlation coefficient R of an original data set requested by a data user in a corresponding smart city task, and when R is not more than alpha, considering the corresponding original data set as a reasonable request data set;
when R is greater than alpha, the corresponding original data group is considered as an abnormal request data group;
where α is a preset coefficient value.
As a further scheme of the invention, the device also comprises an alarm module;
the alarm module is used for marking the abnormal request data set and sending alarm information to prompt the data palm control party to check the abnormal data set;
and when the original data acquisition request sent by the data user comprises an abnormal request data set, the alarm module marks the abnormal request data set and reminds the data palm control party.
As a further scheme of the present invention, in the third step, the original data group stored in the data storage module is compared with the original data group contained in the original data acquisition request sent by each data user, and the original data group which does not exist in the data storage module is marked as the original data group to be acquired;
acquiring a correlation coefficient R of an original data group to be acquired corresponding to different task groups in an original data acquisition request range sent by a data user, and summing to obtain a general coefficient U corresponding to the original data;
acquiring a general coefficient U of each group of original data;
acquiring a sensitivity coefficient X and a general coefficient U of each original data group to be acquired;
calculating to obtain an auditing coefficient H of each original data group to be acquired according to a formula H = gamma 1*X + gamma 2*U;
wherein γ 1 and γ 2 are predetermined coefficients.
And for each original data group to be acquired, sequencing each original data group to be acquired according to the sequence of the audit coefficient H from large to small, transmitting the sequence of the original data groups to be acquired to an audit module, and auditing data acquisition parties and data use parties corresponding to each original data group to be acquired by the audit module according to the sequence.
As a further aspect of the present invention, the method for calculating the sensitivity coefficient X includes:
acquiring the attacked times c1 of the original data group and the attacked times c2 of the data set corresponding to each smart city task, and calculating to obtain a sensitive value G corresponding to the original data group according to a formula G = c1+ c 2/v;
c1 and c2 are respectively the corresponding original data set and the total number of times of attacking of the data set corresponding to the smart city task;
calculating a mutation value G of the corresponding original data group according to a formula G = G/T, wherein T is a preset time value;
and calculating the sensitivity coefficient X corresponding to the original data group according to the formula X = G G/beta 1, wherein beta 1 is a preset coefficient.
As a further scheme of the invention, the value of T is seven days.
The invention has the beneficial effects that:
(1) When the smart city is constructed, corresponding original data required for processing smart city tasks in the prior art are acquired, so that the necessity of each original data set for one smart city task is judged in the subsequent process, whether the original data set requested by a data user is matched with the smart city task required by the data user can be actively judged, an abnormal original data set request is actively prompted, the auditing and management supervision of the data user is enhanced, and the data user is prevented from abusing the original data set;
(2) The method has the advantages that the original data group to be acquired with a large auditing coefficient has the characteristics of wide application range and high confidentiality value, so that the potential risk is larger after the method is opened, more and more complex auditing processes and longer auditing period are realized, when the related work of the original data group to be acquired is processed, the data acquisition party and the data use party corresponding to the original data group to be acquired with a large auditing coefficient are preferentially audited, and the problems that the whole involved auditing process of the original data group to be acquired is prolonged and the construction progress of the smart city is influenced can be avoided.
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The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a framework structure of a smart city information security system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
A smart city information security system, as shown in fig. 1, comprising:
the data storage module is used for storing the original data group;
the alarm module is used for marking the abnormal request data set and sending alarm information to prompt the data palm control party to check the abnormal data set;
the auditing module is used for auditing the collector of the original data group to be collected and the data user corresponding to the original data group to be collected, so as to avoid the problems of inconsistent qualification of the collector of the original data group to be collected, low collection efficiency, poor quality of the collection result, inconsistent qualification of the data user and the like;
the data processing module is used for processing and analyzing the original data groups in the data storage module, judging whether each original data group belongs to a reasonable request data group or an abnormal request data group, and calculating an auditing coefficient H of each original data group;
the working method of the intelligent city information safety guarantee system comprises the following steps:
the method comprises the steps that firstly, when a smart city task is executed, a data set corresponding to the smart city task is obtained, and the data set comprises a plurality of original data sets;
the smart city task is a process of processing a large amount of original data when a certain purpose is required to be met for building a smart city;
the original data group contains the distribution of a kind of data corresponding to the same object in a certain time or a certain space;
secondly, classifying the smart city tasks, and classifying the smart city tasks meeting the same purpose into the same task group;
in the same task group, acquiring the number n of smart city tasks, acquiring the corresponding occurrence times R of each group of original data in the task group in the smart city tasks, and calculating the correlation coefficient R of each original data group corresponding to the task group according to the formula R = R/n;
in the first step and the second step, the historical data are analyzed to obtain corresponding original data required by processing the smart city task in the prior art, so that the subsequent judgment of the necessity of each original data set for the smart city task is facilitated;
thirdly, calculating to obtain the sensitivity coefficient X of each original data set;
the calculation method of the sensitivity coefficient X comprises the following steps:
acquiring the attacked times c1 of the original data group and the attacked times c2 of the data set corresponding to each smart city task, and calculating to obtain a sensitive value G corresponding to the original data group according to a formula G = c1+ c 2/v;
c1 and c2 are respectively the corresponding original data set and the total number of times of attacking of the data set corresponding to the smart city task;
calculating a mutation value G of the corresponding original data group according to a formula G = G/T, wherein T is a preset time value, and in one embodiment of the invention, the value of T is seven days a week;
calculating a sensitivity coefficient X corresponding to the original data group according to a formula X = G G/beta 1, wherein beta 1 is a preset coefficient;
because part of the original data group has a large hidden value, the data can be attacked in the transmission and use process at ordinary times, the importance of the data can be reflected to a certain extent, the sensitive coefficient X of the original data group is calculated by counting the attacked times of the original data group and combining the attacked historical total data of the original data group and the attacked condition in the recent period of time, and the confidentiality requirement of each original data can be reflected more accurately.
Fourthly, when a city needs to be constructed, acquiring smart city tasks which need to be carried out, acquiring execution units of all smart city tasks according to modes such as bidding and the like, and using the execution units as data users;
the data user side initiates an original data acquisition request to the data palm control side according to the smart city task required to be performed, wherein the original data acquisition request comprises an original data set required by executing the corresponding smart city task;
acquiring a correlation coefficient R of an original data set requested by a data user in a corresponding smart city task, and when R is not more than alpha, considering the corresponding original data set as a reasonable request data set;
when R is greater than alpha, the corresponding original data group is considered as an abnormal request data group;
when an original data acquisition request sent by a data user comprises an abnormal request data set, the alarm module marks the abnormal request data set and reminds a data palm control party to check the abnormal request data set;
wherein α is a preset coefficient value;
fifthly, comparing the original data group stored in the data storage module with the original data group contained in the original data acquisition request sent by each data user, and marking the original data group which does not exist in the data storage module as the original data group to be acquired;
acquiring a correlation coefficient R of an original data group to be acquired corresponding to different task groups in an original data acquisition request range sent by a data user, and summing to obtain a general coefficient U corresponding to the original data;
acquiring a general coefficient U of each group of original data;
acquiring a sensitivity coefficient X and a general coefficient U of each original data group to be acquired;
calculating to obtain an auditing coefficient H of each original data group to be acquired according to a formula H = gamma 1*X + gamma 2*U;
wherein gamma 1 and gamma 2 are preset coefficients;
for each original data group to be acquired, sequencing each original data group to be acquired according to the sequence of the audit coefficient H from large to small, transmitting the sequence of the original data groups to be acquired to an audit module, and auditing data acquisition parties and data use parties corresponding to each original data group to be acquired by the audit module according to the sequence;
for an original data group to be acquired, acquiring the original data group to be acquired by a proper unit, wherein the unit for acquiring the original data group to be acquired is called a data acquisition party, and the data acquisition party and a data use party can be mutually overlapped or mutually independent;
the original data group to be acquired with the large auditing coefficient H has the characteristics of wide application range and high confidentiality value, so that the potential risk is larger after the original data group to be acquired is opened, more and more complex auditing processes are realized, when the related work of the original data group to be acquired is processed, the data acquisition party and the data use party corresponding to the original data group to be acquired with the large auditing coefficient H are preferentially audited, and the problem that the overall involved auditing process of the original data group to be acquired is prolonged and the progress of construction of a smart city is influenced can be avoided.
When the intelligent city is built, whether the original data set requested by the data user is matched with the intelligent city task required by the data user can be actively judged, the abnormal original data set request is actively prompted, and the data user is prevented from abusing the original data set.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is illustrative and explanatory only and is not intended to be exhaustive or to limit the invention to the precise embodiments described, and various modifications, additions, and substitutions may be made by those skilled in the art without departing from the scope of the invention or exceeding the scope of the claims.
Claims (5)
1. The utility model provides a wisdom city information safety guarantee system which characterized in that includes:
the data storage module is used for storing the original data group;
the auditing module is used for auditing a data acquisition party of the original data group to be acquired and a data use party corresponding to the original data group to be acquired;
the working method of the intelligent city information safety guarantee system comprises the following steps:
the method comprises the steps that firstly, when a smart city task is executed, a data set corresponding to the smart city task is obtained, and the data set comprises a plurality of original data sets;
secondly, classifying the smart city tasks, and classifying the smart city tasks meeting the same purpose into the same task group;
in the same task group, acquiring the number n of smart city tasks, acquiring the corresponding occurrence times R of each group of original data in the task group in the smart city tasks, and calculating the correlation coefficient R of each original data group corresponding to the task group according to the formula R = R/n;
thirdly, when a city needs to be constructed, acquiring a smart city task which needs to be carried out, and taking the execution unit as a data user;
the data user side initiates an original data acquisition request to the data palm control side according to the smart city task required to be performed, wherein the original data acquisition request comprises an original data set required by executing the corresponding smart city task;
acquiring a correlation coefficient R of an original data set requested by a data user in a corresponding smart city task, and when R is not more than alpha, considering the corresponding original data set as a reasonable request data set;
when R is greater than alpha, the corresponding original data group is considered as an abnormal request data group;
where α is a preset coefficient value.
2. The smart city information security system according to claim 1, further comprising an alarm module;
the alarm module is used for marking the abnormal request data set and sending alarm information to prompt the data palm control party to check the abnormal data set;
and when the original data acquisition request sent by the data user comprises an abnormal request data set, the alarm module marks the abnormal request data set and reminds the data palm control party.
3. The smart city information security guarantee system according to claim 1, wherein in the third step, the original data set stored in the data storage module is compared with the original data set included in the original data acquisition request sent by each data user, and the original data set not existing in the data storage module is marked as the original data set to be collected;
acquiring a correlation coefficient R of an original data group to be acquired corresponding to different task groups in an original data acquisition request range sent by a data user, and summing to obtain a general coefficient U corresponding to the original data;
acquiring a general coefficient U of each group of original data;
acquiring a sensitivity coefficient X and a general coefficient U of each original data group to be acquired;
calculating to obtain an auditing coefficient H of each original data group to be acquired according to a formula H = gamma 1*X + gamma 2*U;
wherein gamma 1 and gamma 2 are preset coefficients;
and for each original data group to be acquired, sequencing each original data group to be acquired according to the sequence of the audit coefficient H from large to small, transmitting the sequence of the original data groups to be acquired to an audit module, and auditing data acquisition parties and data use parties corresponding to each original data group to be acquired by the audit module according to the sequence.
4. The system of claim 3, wherein the sensitivity factor X is calculated by:
acquiring the attacked times c1 of the original data group and the attacked times c2 of the data set corresponding to each smart city task, and calculating to obtain a sensitive value G corresponding to the original data group according to a formula G = c1+ c 2/v;
c1 and c2 are respectively the corresponding original data set and the total number of times of attacking of the data set corresponding to the smart city task;
calculating a mutation value G of the corresponding original data group according to a formula G = G/T, wherein T is a preset time value;
and calculating the sensitivity coefficient X corresponding to the original data group according to the formula X = G G/beta 1, wherein beta 1 is a preset coefficient.
5. The smart city information security system of claim 4, wherein T takes seven days.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018036324A1 (en) * | 2016-08-26 | 2018-03-01 | 中兴通讯股份有限公司 | Smart city information sharing method and device |
| US20190273783A1 (en) * | 2016-08-10 | 2019-09-05 | Chengdu Qinchuan Iot Technology Co., Ltd. | Smart City System Architecture |
| CN112232997A (en) * | 2020-04-29 | 2021-01-15 | 广元量知汇科技有限公司 | User request data processing method for smart city |
| CN112532705A (en) * | 2020-11-20 | 2021-03-19 | 季速漫 | Smart city service system based on big data |
| CN115293599A (en) * | 2022-08-10 | 2022-11-04 | 成都西南锦云大数据有限公司 | Big data acquisition of wisdom city system that gathers |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190273783A1 (en) * | 2016-08-10 | 2019-09-05 | Chengdu Qinchuan Iot Technology Co., Ltd. | Smart City System Architecture |
| WO2018036324A1 (en) * | 2016-08-26 | 2018-03-01 | 中兴通讯股份有限公司 | Smart city information sharing method and device |
| CN112232997A (en) * | 2020-04-29 | 2021-01-15 | 广元量知汇科技有限公司 | User request data processing method for smart city |
| CN112532705A (en) * | 2020-11-20 | 2021-03-19 | 季速漫 | Smart city service system based on big data |
| CN115293599A (en) * | 2022-08-10 | 2022-11-04 | 成都西南锦云大数据有限公司 | Big data acquisition of wisdom city system that gathers |
Non-Patent Citations (1)
| Title |
|---|
| 肖静 等: "智慧城市云平台安全保护体系研究", 现代计算机, no. 14, pages 51 - 56 * |
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