TWI664538B - Big data security fusion method without leaking privacy - Google Patents

Abstract

The invention relates to a big data security fusion method, which includes: a first party and a second party negotiating an associated field, respective data items and ordering rules; and based on the respective data items required from the first data set, The first data set to be fused and the second data set to be fused are selected in the second data set; the first data set to be fused and the second data set to be fused are sorted according to the sorting rule, and the data corresponding to the associated fields are respectively Remove from the first to-be-fused data set and the second to-be-fused data set; submit the first to-be-fused data set and the second to-be-fused data set to the third-party computing platform to form a fused data set; Fusion data sets are analyzed and calculated to generate result data sets. While realizing big data fusion, it effectively prevents the leakage of private data and promotes the sharing of information on the premise of ensuring data security.

Description

Big data security fusion method without leaking privacy

The invention relates to a big data security fusion method.

With the introduction of the national "Internet +" strategy, the need for big data fusion between industries is becoming more urgent. However, on the one hand, different organizations welcome the sharing of big data. The introduction of the fusion of different types of data can generate new analysis results, and the value of the data will therefore have a multiplier effect. On the other hand, the two parties are concerned about the process of data fusion. There are concerns about the leakage of private data in China, because the final analysis result is often only a statistical conclusion, but in the process of big data fusion calculation, all the details of the data have to be exposed to each other. This problem has become a major obstacle to the collaboration and sharing of big data among industries.

Therefore, those skilled in the art expect to obtain a reliable big data security fusion method that effectively masks private data.

An object of the present invention is to provide a big data security fusion method for effectively masking private data.

To achieve the above object, the present invention provides a technical solution as follows: A method for securely merging big data, for merging a first data set stored by a first party and a second data set stored by a second party, the method includes the following steps : A), the first party and the second party negotiate the relevant fields, their respective data items, and the sorting rules; b), based on their respective data items, they are filtered from the first data set and the second data set, respectively The first to-be-fused data set and the second to-be-fused data set are generated; c), the first to-be-fused data set and the second to-be-fused data set are sorted according to sorting rules, and the data corresponding to the associated fields are respectively ranked from the first The first to-be-fused data set and the second to-be-fused data set are eliminated; d), the first party and the second party respectively submit the first to-be-fused data set and the second to-be-fused data set to the third-party computing platform to form a fused Data set; e) The third-party computing platform analyzes and calculates the fused data set to generate a result data set.

Preferably, the third-party computing platform is independent of the first party and the second party, respectively.

Preferably, after the analysis and calculation are completed, the first data set to be fused and the second data set to be fused are deleted from the computing system.

The big data security fusion method provided by the embodiment of the present invention effectively prevents the leakage of private data while realizing big data fusion, promotes information sharing under the premise of ensuring data security, and broadens the application breadth and depth. In addition, the above-mentioned big data security fusion method is simple to implement and low in implementation cost, which is conducive to promotion and application in the industry.

S10 ~ S50‧‧‧step

FIG. 1 is a schematic flowchart of a big data security fusion method provided by a first embodiment of the present invention.

It should be noted that according to the embodiments disclosed in the present invention, the first party stores the first data set in the first database, and the second party stores the second data set in the second database.

The first and second data sets record different information, for example, information on the activities of multiple users on different occasions. The first and second data sets have the intersection of information, for example, the user's identity information, which can be extracted as related fields.

The present invention provides various embodiments for performing big data fusion on the first and second data sets.

As shown in FIG. 1, a first embodiment of the present invention provides a method for securely integrating big data, which includes the following steps: Step S10, the first party and the second party perform an association field, each required data item, and a sorting rule. Negotiation.

Specifically, the first party and the second party have a negotiation session, and reach agreement on the associated fields, their respective data items, and the ordering rules.

The data items required by each of them include data items that the first party expects to obtain indirectly from the second party in the data fusion, and data items that the second party expects to obtain indirectly from the first party in the data fusion. Through their respective needs In the negotiation session, you can determine the information about which users the first party and the second party care about, and further agree on the identity information of these users.

The associated field can represent the intersection of information in the first and second data sets, which can be directly taken from any one or more of the following information: the user's identity information; the user's card information; and / or, the only To identify the user ’s other identifying information.

The sorting rules determine the order in which the specific data sets to be fused are sorted in the subsequent fusion process. Once determined, this sorting rule cannot be changed arbitrarily unless it is changed through a renegotiation session. According to the determined sorting rules, the correspondence between the data items in the first and second data sets to be fused can also be determined.

The negotiation session can be initiated by the first or second party and the other party responds. Alternatively, the negotiation session may be initiated by an independent entity module different from the first party and the second party. After receiving the instruction, the first party and the second party directly conduct the negotiation session, and notify the entity after the negotiation session is completed. Module.

In step S20, a first data set to be fused and a second data set to be fused are respectively selected from the first data set and the second data set based on the required data items.

Specifically, based on the respective required data items determined by the negotiation session, the first data set to be fused can be selected from the first data set, and the second data set to be fused is selected from the second data set. It can be understood that the first data set to be fused and the second data set to be fused have a quantity phase The same data item, and each data item in the first to-be-fused data set can find a corresponding data item in the second to-be-fused data set, and vice versa.

Step S30: Sort the first to-be-fused data set and the second to-be-fused data set according to the sorting rule, and remove the data corresponding to the associated fields from the first to-be-fused data set and the second to-be-fused data set, respectively.

This step S30 specifically includes a sorting step and a culling step.

According to a specific implementation, the sorting step may include: the first party and the second party sort the first to-be-fused data set and the second to-be-fused data set according to the sorting rule, respectively.

The removing step may include: the first party and the second party respectively delete the data corresponding to the associated fields from the first to-be-fused data set and the second to-be-fused data set.

By performing the culling step, the first and second to-be-fused data sets no longer include user identity information, which effectively masks the privacy information; and by performing the sorting step, one of the data items in the first and second to-be-fused data sets There is a clear one-to-one correspondence between them.

Step S40: The first party and the second party respectively submit the first to-be-fused data set and the second to-be-fused data set to a computing platform set up by a third party to form a fused data set.

Specifically, the first party submits the first to-be-fused data set obtained after performing the sorting step and the rejection step to a computing platform erected by a third party through a dedicated communication line, and at the same time, the second party performs a similar operation. its In China, third-party computing platforms are independent of the first and second parties.

Subsequently, according to the sequence obtained by executing the above sorting steps, the data items in the first to-be-fused data set are combined with the data items in the second to-be-fused data set in a one-to-one correspondence to generate new data items, and then form a fused Data set.

The resulting fused data set includes both user activity information from the first party and user activity information from the second party, but does not include user identity information, so for third-party vendors, they cannot know which Users performed these activities.

Step S50: The third-party computing platform analyzes and calculates the fused data set to generate a result data set.

Through this step S50, the third-party computing platform can analyze and calculate the fused data set to generate a result data set. The result data set can be the result of analysis and statistics, which is completely different from the first and second data sets to be fused. The result data set can be fed back to the first and second parties, and the first and second parties cannot restore the original data from the result data set.

Further, after the above analysis and calculation are completed, the third-party computing platform can delete the first data set to be fused and the second data set to be fused, which is more conducive to protecting the security and privacy of the data.

The big data security fusion method provided by this embodiment masks the user's identity information while realizing big data fusion, thereby effectively preventing the leakage of private data. This big data fusion method is safe, reliable, and easy to implement.

According to a further improved implementation manner of the foregoing embodiment, in Step S10 may further include: the first party submits to the second party a field related to the user's privacy information in the first data set or a field that needs to be protected. Correspondingly, step S30 further includes: excluding the data corresponding to the field related to the user's private information or the field that needs to be protected from the first to-be-fused data set.

Similarly, the second party may also propose to the first party fields in the second data set that relate to the user's private information or fields that need to be protected.

This improved implementation method provides enhanced protection of user privacy information, and is particularly suitable for use in places with high requirements for data protection.

The above description is only directed to the preferred embodiments of the present invention, and is not intended to limit the protection scope of the present invention. Those skilled in the art can make various modified designs without departing from the idea of the present invention and the accompanying claims.

Claims (5)

A big data security fusion method for fusing a first data set stored by a first party and a second data set stored by a second party, the method includes the following steps: a), the first party and the second party Negotiate the associated fields, their respective data items, and sorting rules; b), based on the respective required data items, filter out the first data set to be fused, the first data set, the first Two to-be-fused data sets; c) sort the first to-be-fused data set and the second to-be-fused data set respectively according to the sorting rule, and separate the data corresponding to the associated field from the first to-be-fused data set And the second to-be-fused data set is eliminated; d) the first party and the second party respectively submit the first to-be-fused data set and the second to-be-fused data set to a third-party computing platform to form a fused data set; e) The third-party computing platform analyzes and calculates the fused data set to generate a result data set, wherein the fused data set includes user activity information from the first party and information from the second party. User activity information and does not include user identity information. The method according to item 1 of the scope of patent application, wherein the third-party computing platform is independent of the first party and the second party, respectively. The method according to item 1 of the scope of patent application, wherein step e) further comprises: after the analysis and calculation is completed, removing the first to-be-fused data set and the second to-be-fused data set from the third-party computing platform delete. The method according to claim 1 in the patent application scope, wherein the first data set and the second data set respectively record different activity information of a plurality of users, and the associated fields include: the identity information of the user; Card information; and / or uniquely identifying user information. The method according to claim 4 in the patent application scope, wherein the step a) further comprises: the first submitting to the second party a field in the first data set related to the user's private information; the step c) further comprises: The data corresponding to the field related to the user's private information is removed from the first to-be-fused data set.