CN109992977B - Data anomaly point cleaning method based on safe multi-party computing technology - Google Patents

Data anomaly point cleaning method based on safe multi-party computing technology Download PDF

Info

Publication number
CN109992977B
CN109992977B CN201910156492.6A CN201910156492A CN109992977B CN 109992977 B CN109992977 B CN 109992977B CN 201910156492 A CN201910156492 A CN 201910156492A CN 109992977 B CN109992977 B CN 109992977B
Authority
CN
China
Prior art keywords
data
server
party
thre
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910156492.6A
Other languages
Chinese (zh)
Other versions
CN109992977A (en
Inventor
刘雪峰
杨烨
裴庆祺
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xidian University
Original Assignee
Xidian University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xidian University filed Critical Xidian University
Priority to CN201910156492.6A priority Critical patent/CN109992977B/en
Publication of CN109992977A publication Critical patent/CN109992977A/en
Application granted granted Critical
Publication of CN109992977B publication Critical patent/CN109992977B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Bioethics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computer Security & Cryptography (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Databases & Information Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The invention belongs to the technical field of information security, and discloses a data abnormal point cleaning method based on a secure multi-party computing technology, which comprises the following steps: unifying the data of the two participants A and B into a matrix format, wherein the data have the same dimensionality, and the last dimensionality is the AVF value of the data; the method comprises the following steps that a participant A and a participant B encrypt a data matrix by using a Yao's encryption algorithm in a secure multiparty computing algorithm ABY; and the server A and the server B carry out data anomaly point cleaning on the encrypted data sets uploaded by all the participants. The invention combines the safe multi-party computing technology and the AVF abnormal value detection algorithm, utilizes the ABY algorithm of the existing safe multi-party computing tool to realize the high-efficient detection of high-dimensional data, and utilizes the Yao's encryption algorithm in the safe multi-party computing technology to ensure the safety with equivalent privacy of each party of data on the premise of ensuring certain efficiency.

Description

Data anomaly point cleaning method based on safe multi-party computing technology
Technical Field
The invention belongs to the technical field of information security, and particularly relates to a data anomaly point cleaning method based on a secure multi-party computing technology.
Background
Currently, the closest prior art: the combined data source refers to the fact that in the machine learning training process, a plurality of participants have the same type of data, the data are fused, the scale of a training data set can be enlarged, and the accuracy of a model training result is improved. The quality of the model depends on the scale and quality of the data set, so that the joint data source learning becomes a great trend of the machine learning development. However, with the advantages of the training of the joint data sources, the new problem of the security protection of the data privacy of the multiple data sources is solved, and in some scenes, the data owned by each participant may be privacy-sensitive, for example, some commercial data or privacy information of some customers, such as medical information or property information, etc., and the requirement of the data on the privacy protection is extremely high, and it is naturally difficult to share the data at will.
With the increasing demand for data fusion, algorithms aimed at protecting data privacy are also emerging. For example, a method of adding a trusted third party is adopted, a plurality of participants jointly authenticate one trusted third party, respective plaintext data is uploaded to the third party, and the third party performs tasks such as data cleaning and training, and the trusted third party is often an organization with public trust or a cloud computing provider providing charging services. The method has the advantages that the privacy protection of the data is realized, and the purpose of fusing the data is also achieved. However, the algorithm has certain security risk, a trusted third party is usually honest and curious, and if unpredictable data leakage occurs in the process of collecting and processing the data or a malicious third party steals data information, serious consequences are often caused.
With the integration of technologies in various fields, the thinking of cryptography is applied to the field of joint data source training, namely, the data of each participant is encrypted by using a mature encryption algorithm, and then the encrypted data is gathered and sent to a trusted third party, the trusted third party does not have sensitive plaintext data and only has ciphertext data which seems to have no practical significance after encryption, the encryption algorithm usually adopts homomorphic encryption, namely after the plaintext is encrypted, how to operate the ciphertext is equal to that of performing the same operation on the plaintext, and the encryption method ensures the feasibility of ciphertext training, so that the privacy of the data is greatly ensured. However, the algorithm also has a practical problem, the biggest problem is the game between safety and efficiency, the result obtained by the existing homomorphic encryption algorithm usually needs to consume a large amount of time and computing resources, and the algorithm has extremely low use efficiency under the condition that the privacy requirement is not so high, and is not suitable for being popularized in a large amount.
In the prior art, an algorithm for cleaning abnormal points of multi-data source combined data by using a homomorphic encryption algorithm is provided, each party of data is encrypted by using the homomorphic encryption algorithm, and then an AVF abnormal point detection algorithm is adopted to screen and clean abnormal points in a data set, but due to the limitation of the efficiency of homomorphic encryption, the time and the computing resources required by encryption and decryption are more, so that the algorithm has lower relative computing efficiency and cannot meet the requirement of processing a large amount of data; the second prior art provides a privacy protection data cleaning scheme based on an LOF abnormal point detection algorithm, but because the privacy protection data cleaning scheme determines whether data is abnormal points or not based on data distribution density, if the dimensionality of the data is high, the existence of the abnormal points cannot be effectively distinguished according to the difference of the distribution density, and therefore the second prior art has the problem of low processing efficiency in the face of high-dimensional data sets.
In summary, the problems of the prior art are:
(1) The existing algorithm for cleaning the abnormal points of the multi-data-source combined data by using a homomorphic encryption algorithm is low in calculation efficiency and cannot meet the requirement of processing a large amount of data.
(2) The existing privacy protection data cleaning scheme based on the LOF abnormal point detection algorithm has the problem of low processing efficiency when facing a high-dimensional data set.
In view of the above problems, a new technique capable of balancing the computational efficiency and security is needed, which can improve the low efficiency and high energy consumption of the conventional homomorphic encryption algorithm, and can also ensure the necessary data privacy security requirements, and at the same time, in order to better adapt to the actual implementation example, it is also needed to support the processing of high-dimensional data.
The significance of solving the technical problems is as follows:
after the problems existing in the technology are improved, the algorithm can be more suitable for the actual use environment, the actual use efficiency is improved, the implementability of the algorithm is increased, and the privacy security of the sensitive data can be better protected.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a data anomaly point cleaning method based on a safe multi-party computing technology.
The invention is realized in such a way that a data abnormal point cleaning method based on a safe multi-party computing technology comprises the following steps:
step one, unifying data of two parties A and B into a matrix format, wherein the data have the same dimensionality, and the last dimension is the AVF value of the data;
secondly, encrypting the data matrix by the participant A and the participant B by using a Yao's encryption algorithm in a secure multi-party computing algorithm ABY;
and thirdly, the server A and the server B perform data anomaly point cleaning on the encrypted data sets uploaded by the participants.
Further, the first participant a and the participant B unify the proprietary data set formats as specified:
Figure BDA0001983086540000031
wherein D is 1 A dataset matrix of N x (M + 1) representing party A, a ij Representing arbitrary data in participant A dataset, avf ai AVF values representing participant A ith data, i ∈ [1, N]，j∈[1，M]，M，N∈N + ；D 2 A dataset matrix representing P x (M + 1) of party B, B kj Represents arbitrary data in participant B dataset, avf bk AVF values for kth data, k ∈ [1]，j∈[1，M]，M，P∈N + . Where the data dimensions of both participants are the same.
Further, the encrypting the own data set by the second party a and the second party B according to the specification specifically includes:
1) Encrypting data set D of participant A by Yao's encryption algorithm in ABY algorithm by utilizing secure multi-party computation 1 And (3) encryption:
Figure BDA0001983086540000032
wherein,
Figure BDA0001983086540000033
a section representing that the encrypted data set is handed to the server a,
Figure BDA0001983086540000034
denotes the portion of the encrypted data set that is handed over to server B, enc denotes Yao's encryption algorithm, D 1 Representing participantsA is a data set;
specifically, each element is encrypted according to the following formula:
Figure BDA0001983086540000041
Figure BDA0001983086540000042
wherein,
Figure BDA0001983086540000046
a section showing that the encrypted data is handed to the server a,
Figure BDA0001983086540000047
a section showing that the encrypted data is handed to the server B, a ij Any data representing party a;
Figure BDA0001983086540000048
the AVF value representing the encrypted ith piece of data of party a is handed to a portion of server a,
Figure BDA0001983086540000049
the part in which the AVF value of the ith piece of data representing the encrypted participant A is handed over to the server B, AVF ai AVF value of the ith piece of data representing party a;
2) The encrypted data set of party a is represented by:
Figure BDA0001983086540000043
Figure BDA0001983086540000044
wherein, X 10 An encrypted data set, X, representing a party A held by a server A 11 Representing a parameter held by Server BEncrypted dataset with party A, i ∈ [1, N ]]，j∈[1，M]，M，N∈N + ；
3) Encrypting data set D of participant B using Yao's encryption algorithm in ABY algorithm using secure multi-party computation 2 And (3) encryption is carried out:
Figure BDA0001983086540000045
wherein,
Figure BDA00019830865400000410
a section indicating that the encrypted data set is handed to the server a,
Figure BDA00019830865400000411
denotes the portion of the encrypted data set that is handed over to server B, enc denotes Yao's encryption algorithm, D 2 A data set representing party B;
specifically, each element is encrypted according to the following formula:
Figure BDA0001983086540000051
Figure BDA0001983086540000052
wherein,
Figure BDA0001983086540000056
a section showing that the encrypted data is handed to the server a,
Figure BDA0001983086540000057
a section showing that the encrypted data is handed to the server B, B kj Any data representing party a;
Figure BDA0001983086540000058
the AVF value representing the encrypted kth piece of data of party B is handed over to part of server a,
Figure BDA0001983086540000059
part of server B that gives AVF value of encrypted kth data of party B to bk AVF value representing the kth piece of data for party B;
4) The encrypted data set of party B is represented by:
Figure BDA0001983086540000053
Figure BDA0001983086540000054
wherein, X 20 An encrypted data set, X, representing a party B held by a server A 21 Represents the encrypted data set of party B held by Server B, k ∈ [1]，j∈[1，M]，M，P∈N + ；
5) And the participant A and the participant B respectively upload the encrypted data to corresponding servers.
Further, the third step of performing, by the server a and the server B, data anomaly point cleaning on the encrypted data sets uploaded by the participants specifically includes:
1) The server A extracts the last one-dimensional data in the encrypted data set of the party A taken by the server A:
Figure BDA0001983086540000055
server A uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair A 10 And (3) sequencing:
A′ 10 ＝Sort(A 10 )；
wherein, A 10 Last-dimensional data, A ', in the encrypted data set representing party A owned by Server A' 10 Is represented by A 10 Sort the data in descending order, sort () represents Yao's encryption algorithmThe sorting algorithm of (1);
with A 10 Taking X as a reference 10 Also sorted simultaneously, i.e. according to A 10 Descending order of X 10 And after the sorting is finished:
Figure BDA0001983086540000061
wherein, X' 10 Is represented by X 10 Last one-dimensional data, i.e. A 10 Submitting the data set of the server A for the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 10 The data in (1) is compared with Thre in order:
Res i ＝Comp(A′ 10i ，Thre)；
wherein, A' 10i Represents A' 10 Element in (1, N), i ∈ []，N∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm i Is represented by A' 10i If Res is compared with Thre i Value is 1 and represents A' 10i More than or equal to Thre; if Res i Value is 0 and represents A' 10i < Thre, A' 10 The data in (2) is compared with Thre in sequence until Res i =0, stop comparison, compare X' 10 First i row data retention in (1):
Figure BDA0001983086540000062
where I = I, is the first I row of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 10 Cleaning the final data and then completing the data set of the participant A owned by the server A;
2) The server A extracts the last one-dimensional data in the encrypted data set of the party B taken by the server A:
Figure BDA0001983086540000071
server A uses the sorting algorithm in Yao's encryption algorithm in security encryption algorithm ABY to pair A 20 And (3) sequencing:
A′ 20 ＝Sort(A 20 )；
wherein A is 20 Last-dimensional data, A 'in encrypted data set representing party B owned by Server A' 20 Is shown as A 20 Sort the data according to the descending order, sort () represents the sorting algorithm in Yao's encryption algorithm;
with A 20 Taking X as a reference 20 Also sorted simultaneously, i.e. according to A 20 Descending order of X 20 And after the sorting is finished:
Figure BDA0001983086540000072
wherein, X' 20 Is represented by X 20 Last one-dimensional data, i.e. A 20 Submitting the data set of the server A for the participant B after the reference descending sorting is completed, wherein k belongs to [1, P ]]，j∈[1，M]，M，P∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 20 The data in (1) is compared with Thre in order:
Res k ＝Comp(A′ 20k ，Thre)；
wherein, A' 20k Represents A' 20 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm k Represents A' 20k If Res is the result of comparison with Thre k Value is 1 and represents A' 20k More than or equal to Thre; if Res k Value is 0 and represents A' 20k < Thre, A' 20 The data in (2) is compared with Thre in sequence until Res k =0, stop comparison, compare X' 20 First k rows of data retention:
Figure BDA0001983086540000073
where K = K, is the first K rows of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 20 The data set of the participant B owned by the server A after the final data cleaning is finished;
3) The server B extracts the last one-dimensional data in the encrypted data set of the party A taken by the server B:
Figure BDA0001983086540000081
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair a 11 And (3) sequencing:
A′ 11 ＝Sort(A 11 )；
wherein A is 11 Last-dimensional data, A 'in encrypted data set representing party A owned by server B' 11 Is shown as A 11 Sort the data according to the descending order, sort () represents the sorting algorithm in Yao's encryption algorithm;
with A 11 Taking X as a reference 11 Also sorted simultaneously, i.e. according to A 11 Descending order of X 11 After the sorting is finished:
Figure BDA0001983086540000082
wherein, X' 11 Is represented by X 11 Last one-dimensional data, i.e. A 11 The data set submitted to the server B by the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 11 The data in (1), comparing size to Thre in order:
Res i ＝Comp(A′ 11i ，Thre)；
wherein, A' 11i Represents A' 11 Element in (1, N), i ∈ []，N∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm i Is represented by A' 11i If Res is the result of comparison with Thre i Value is 1 and represents A' 11i More than or equal to Thre; if Res i Value is 0 and represents A' 11i < Thre, A' 11 The data in (1) is compared with Thre in sequence until Res i =0, stop comparison, X' 11 First i row data retention in (1):
Figure BDA0001983086540000091
where I = I, is the first I row of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 11 The data set of the participant A owned by the server B after the final data cleaning is finished;
4) The server B extracts the last one-dimensional data in the encrypted data set of the party B taken by the server B:
Figure BDA0001983086540000092
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair A 21 And (3) sequencing:
A′ 21 ＝Sort(A 21 )；
wherein A is 21 Last-dimensional data, A 'in encrypted data set representing party B owned by server B' 21 Is represented by A 21 Sort the completed data in descending order, sort () represents the sorting algorithm in Yao's encryption algorithm.
With A 21 Taking X as a reference 21 Also sorted simultaneously, i.e. according to A 21 Descending order of X 21 And after the sorting is finished:
Figure BDA0001983086540000093
wherein, X' 21 Is represented by X 21 Last one-dimensional data, i.e. A 21 Submitting the data set of the server B for the participant B after the reference descending sorting is completed, wherein k belongs to [1]，j∈[1，M]，M，P∈N + 。
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 21 The data in (1) is compared with Thre in order:
Res k ＝Comp(A′ 21k ，Thre)；
wherein, A' 21k Is represented by A' 21 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithms k Represents A' 21k If Res is the result of comparison with Thre k Value is 1 and represents A' 21k More than or equal to Thre; if Res k Value is 0 and represents A' 21k < Thre, A' 21 The data in (1) is compared with Thre in sequence until Res k =0, stop comparison, compare X' 21 The first k rows of data retention in (1):
Figure BDA0001983086540000101
where K = K, j ∈ [1,M ] for the top K rows of data retained after sorting]，M∈N + ，X″ 21 Cleaning the final data and then finishing the data set of the participant B owned by the server B;
5) The final X ″) 10 ，X″ 11 ，X″ 20 ，X″ 21 And cleaning the finished data set for the final data.
Another object of the present invention is to provide a machine learning system applying the data outlier cleaning method based on secure multi-party computing technology.
In summary, the advantages and positive effects of the invention are: the invention combines the safe multi-party computing technology and the AVF abnormal value detection algorithm, utilizes the ABY algorithm of the existing safe multi-party computing tool to realize the high-efficiency detection of high-dimensional data, and utilizes the Yao's encryption algorithm in the safe multi-party computing technology to ensure the safety with equivalent privacy of each party of data on the premise of ensuring certain efficiency.
TABLE 1 comparison of technical Properties
Figure BDA0001983086540000102
Drawings
Fig. 1 is a flowchart of a data anomaly point cleaning method based on a secure multi-party computing technology according to an embodiment of the present invention.
Fig. 2 is a scene schematic diagram of an embodiment provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problem that the existing algorithm for cleaning abnormal points of multi-data source combined data by using a homomorphic encryption algorithm is low in calculation efficiency and cannot meet the requirement of processing a large amount of data; the existing privacy protection data cleaning scheme based on the LOF abnormal point detection algorithm has the problem of low processing efficiency when facing a high-dimensional data set. The method is mainly used for realizing a safety data abnormal point cleaning algorithm under the combined data source environment; based on a safe multi-party computing technology, the data abnormal point cleaning work under the condition that a plurality of data sources are combined with a machine learning scene under the condition of privacy protection is realized.
The application of the principles of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the method for cleaning data anomaly points based on secure multi-party computing technology according to the embodiment of the present invention includes the following steps:
s101: participant a and participant B unify the proprietary data set formats as specified: unifying the data of the two participants A and B into a matrix format, wherein the data have the same dimensionality, and the last dimensionality is an Attribute Value Frequency (AVF) Value of the data;
s102: party a and party B encrypt their own data sets as specified: the method comprises the following steps that a participant A and a participant B encrypt a data matrix by using a Yao's encryption algorithm in a secure multi-party computing algorithm ABY;
s103: and the server A and the server B carry out data anomaly point cleaning on the encrypted data sets uploaded by all the participants.
The application of the principles of the present invention will now be described in further detail with reference to specific embodiments.
The data anomaly point cleaning method based on the safe multi-party computing technology provided by the embodiment of the invention specifically comprises the following steps:
step one, a participant A and a participant B unify the formats of own data sets according to the specification:
Figure BDA0001983086540000121
wherein D is 1 A data set matrix of N (M + 1) representing party A, a ij Represents arbitrary data in participant a dataset, avf ai Attribute Value Frequency (AVF) Value representing participant A ith piece of data, i ∈ [1, N]，j∈[1，M]，M，N∈N + ；D 2 A dataset matrix representing Px (M + 1) of party B, B kj Represents arbitrary data in participant B dataset, avf bk Attribute Value Frequency (AVF) Value representing the kth data of Party A, k ∈ [1, P]，j∈[1，M]，M，P∈N + . Where the data dimensions (i.e., the value of M) of both participants are the same.
Step two, the participant A and the participant B encrypt the own data sets according to the specification:
2a) Data set D of participant A using Yao's encryption algorithm in secure multiparty computing encryption ABY algorithm 1 And (3) encryption:
Figure BDA0001983086540000122
wherein,
Figure BDA0001983086540000125
a section representing that the encrypted data set is handed to the server a,
Figure BDA0001983086540000126
denotes the portion of the encrypted data set that is handed over to server B, enc denotes Yao's encryption algorithm, D 1 A data set representing party a.
Each element is encrypted specifically according to the following equation:
Figure BDA0001983086540000123
Figure BDA0001983086540000124
wherein,
Figure BDA0001983086540000127
a section indicating that the encrypted data is handed to the server a,
Figure BDA0001983086540000128
a section showing that the encrypted data is handed to the server B, a ij Arbitrary data representing party a;
Figure BDA0001983086540000129
the AVF value representing the encrypted ith piece of data of party a is handed to part of server a,
Figure BDA00019830865400001210
the part that the AVF value of the ith piece of data representing the encrypted participant A is handed over to the server B, AVF ai AVF value for the ith piece of data representing party a.
2b) The encrypted data set of party a is represented by:
Figure BDA0001983086540000131
Figure BDA0001983086540000132
wherein, X 10 An encrypted data set, X, representing a party A held by a server A 11 An encrypted data set representing party A held by server B, i ∈ [1, N ]]，j∈[1，M]，M，N∈N + 。
2c) Encrypting data set D of participant B using Yao's encryption algorithm in ABY algorithm using secure multi-party computation 2 And (3) encryption:
Figure BDA0001983086540000133
wherein,
Figure BDA0001983086540000136
a section representing that the encrypted data set is handed to the server a,
Figure BDA0001983086540000137
representing the part of the encrypted data set handed to server B, enc representing the Yao's encryption algorithm, D 2 A data set representing party B.
Specifically, each element is encrypted according to the following formula:
Figure BDA0001983086540000134
Figure BDA0001983086540000135
wherein,
Figure BDA0001983086540000138
a section showing that the encrypted data is handed to the server a,
Figure BDA0001983086540000139
a section showing that the encrypted data is handed to the server B, B kj Arbitrary data representing party a;
Figure BDA00019830865400001310
the AVF value representing the encrypted kth piece of data of party B is handed over to the part of server a,
Figure BDA00019830865400001311
part of server B that gives AVF value of encrypted kth data of party B to bk AVF value representing participant B's kth data.
2d) The encrypted data set of party B is represented by:
Figure BDA0001983086540000141
Figure BDA0001983086540000142
wherein, X 20 An encrypted data set, X, representing a party B held by a server A 21 An encrypted data set representing party B held by Server B, k ∈ [1]，j∈[1，M]，M，P∈N + 。
2e) And the participant A and the participant B respectively upload the encrypted data to corresponding servers.
Step three, the server A and the server B carry out data anomaly point cleaning on the encrypted data sets uploaded by all the participants:
3a) The server a extracts the last one-dimensional data in the encrypted dataset of the party a taken by itself, that is:
Figure BDA0001983086540000143
server A uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair A 10 And (4) sorting:
A′ 10 ＝Sort(A 10 )；
wherein, A 10 Last-dimensional data, A ', in the encrypted data set representing party A owned by Server A' 10 Is represented by A 10 And sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm.
With A 10 Taking X as a reference 10 Also sorted simultaneously, i.e. according to A 10 Descending order of X 10 And after the sorting is finished:
Figure BDA0001983086540000151
wherein, X' 10 Is represented by X 10 Last one-dimensional data, i.e. A 10 The data set submitted to the server A by the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + 。
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 10 The data in (1) is compared with Thre in order:
Res i ＝Comp(A′ 10i ，Thre)；
wherein, A' 10i Represents A' 10 Element in (1, N), i ∈ []，N∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithms i Represents A' 10i If Res is compared with Thre i Value is 1 and represents A' 10i More than or equal to Thre; if Res i Value is 0 and represents A' 10i < Thre, A' 10 The data in (2) is compared with Thre in sequence until Res i =0, stop comparison, compare X' 10 First i row data retention in (1):
Figure BDA0001983086540000152
wherein, I = I, I belongs to [1, M ], and M belongs to N, for the first I row of data reserved after sorting + ，X″ 10 And cleaning the data set of the party A owned by the server A after the final data is cleaned.
3b) The server a extracts the last one-dimensional data in the encrypted data set of the party B taken by itself, that is:
Figure BDA0001983086540000153
server A uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair A 20 And (3) sequencing:
A′ 20 ＝Sort(A 20 )；
wherein A is 20 Last-dimensional data, A 'in encrypted data set representing party B owned by Server A' 20 Is represented by A 20 And sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm.
With A 20 Taking X as a reference 20 Also sorted simultaneously, i.e. according to A 20 Descending order of X 20 And after the sorting is finished:
Figure BDA0001983086540000161
wherein, X' 20 Is represented by X 20 Last one-dimensional data, i.e. A 20 Submitting the data set of the server A for the participant B after the reference descending sorting is completed, wherein k belongs to [1]，j∈[1，M]，M，P∈N + 。
A fixed value Thre (same as Thre above) is defined, which represents a threshold value of AVF value within normal range, and A' 20
The data in (1), comparing size to Thre in order:
Res k ＝Comp(A′ 20k ，Thre)；
wherein, A' 20k Is represented by A' 20 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm k Represents A' 20k If Res is the result of comparison with Thre k Value is 1 and represents A' 20k More than or equal to Thre; if Res k Value is 0 and represents A' 20k < Thre, A' 20 The data in (2) is compared with Thre in sequence until Res k =0, stop comparison, compare X' 20 First k rows of data retention:
Figure BDA0001983086540000162
where K = K, j ∈ [1,M ] for the top K rows of data retained after sorting]，M∈N + ，X″ 20 And cleaning the data set of the participant B owned by the server A after the final data is cleaned.
3c) The server B extracts the last one-dimensional data in the encrypted data set of the party a taken by itself, that is:
Figure BDA0001983086540000171
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair a 11 And (4) sorting:
A′ 11 ＝Sort(A 11 )；
wherein A is 11 Last-dimensional data, A 'in encrypted data set representing party A owned by server B' 11 Is represented by A 11 Sort the completed data in descending order, sort () represents the sorting algorithm in Yao's encryption algorithm.
With A 11 Taking X as a reference 11 Also sorted simultaneously, i.e. according to A 11 Descending order of X 11 After the sorting is finished:
Figure BDA0001983086540000172
wherein, X' 11 Is represented by X 11 Last one-dimensional data, i.e. A 11 The data set submitted to the server B by the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + 。
A fixed value Thre (same as Thre above) is defined, and A 'is a threshold value indicating that the AVF value is within the normal range' 11 The data in (1) is compared with Thre in order:
Res i ＝Comp(A′ 11i ，Thre)；
wherein, A' 11i Represents A' 11 The element in (1, N) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm i Is represented by A' 11i If Res is compared with Thre i Value is 1, represents A' 11i More than or equal to Thre; if Res i Value is 0 and represents A' 11i < Thre, A' 11 The data in (1) is compared with Thre in sequence until Res i =0, stop comparison, X' 11 First i row data retention in (1):
Figure BDA0001983086540000173
where I = I, is the first I row of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 11 And cleaning the data set of the party A owned by the server B after the final data is cleaned.
3d) The server B extracts the last one-dimensional data in the encrypted data set of the party B taken by itself, that is:
Figure BDA0001983086540000181
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair a 21 And (4) sorting:
A′ 21 ＝Sort(A 21 )；
wherein A is 21 Last-dimensional data, A 'in encrypted data set representing party B owned by server B' 21 Is shown as A 21 And sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm.
With A 21 Taking X as a reference 21 Also sorted simultaneously, i.e. according to A 21 Descending order of X 21 After the sorting is finished:
Figure BDA0001983086540000182
wherein, X' 21 Is represented by X 21 Last one-dimensional data, i.e. A 21 Submitting the data set of the server B for the participant B after the reference descending sorting is completed, wherein k belongs to [1]，j∈[1，M]，M，P∈N + 。
A fixed value Thre (same as Thre above) is defined, and A 'is a threshold value indicating that the AVF value is within the normal range' 21 The data in (1), comparing size to Thre in order:
Res k ＝Comp(A′ 21k ，Thre)；
wherein, A' 21k Is represented by A' 21 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm k Is represented by A' 21k If Res is compared with Thre k Value is 1 and represents A' 21k More than or equal to Thre; if Res k Value is 0 and represents A' 21k < Thre, A' 21k The data in (1) is compared with Thre in sequence until Res k =0, stop comparison, X' 21 First k rows of data retention:
Figure BDA0001983086540000191
where K = K, is the first K rows of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 21 Is the most importantAnd after the final data cleaning is finished, the server B owns the data set of the participant B.
3e) The final X ″) 10 ，X″ 11 ，X″ 20 ，X″ 21 And cleaning the finished data set for the final data.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (5)

1. A data abnormal point cleaning method based on a secure multi-party computing technology is characterized by comprising the following steps:
step one, unifying data of two participants A and B into a matrix format, wherein the data have the same dimensionality, and the last dimension is the AVF value of the data;
secondly, encrypting the data matrix by the participant A and the participant B by using a Yao's encryption algorithm in a secure multi-party computing algorithm ABY;
and thirdly, the server A and the server B perform data anomaly point cleaning on the encrypted data sets uploaded by the participants.
2. The secure multi-party computing technology based data anomaly cleansing method according to claim 1, wherein said first step participant a and participant B unify their own data set formats as specified:
Figure FDA0001983086530000011
wherein D is 1 A data set matrix of N (M + 1) representing party A, a ij Representing arbitrary data in participant A dataset, avf ai AVF values representing participant A ith data, i ∈ [1, N]，j∈[1，M]，M，N∈N + ；D 2 A dataset matrix representing P x (M + 1) of party B, B kj Representing arbitrary data in the participant B dataset, avf bk AVF values for kth data, k ∈ [1]，j∈[1，M]，M，P∈N + (ii) a Where the data dimensions of both participants are the same.
3. The method for cleansing data anomaly points based on secure multiparty computing technology according to claim 1, wherein said second step of encrypting the owned data set by party a and party B specifically comprises:
1) Encrypting data set D of participant A by Yao's encryption algorithm in ABY algorithm by utilizing secure multi-party computation 1 And (3) encryption:
Figure FDA0001983086530000012
wherein,
Figure FDA0001983086530000013
a section representing that the encrypted data set is handed to the server a,
Figure FDA0001983086530000014
denotes the portion of the encrypted data set that is handed over to server B, enc denotes Yao's encryption algorithm, D 1 A data set representing party a;
each element is encrypted specifically according to the following equation:
Figure FDA0001983086530000021
Figure FDA0001983086530000022
wherein,
Figure FDA0001983086530000023
means plusThe encrypted data is handed over to a part of the server a,
Figure FDA0001983086530000024
a section showing that the encrypted data is handed to the server B, a ij Any data representing party a;
Figure FDA0001983086530000025
the AVF value representing the encrypted ith piece of data of party a is handed to part of server a,
Figure FDA0001983086530000026
the part in which the AVF value of the ith piece of data representing the encrypted participant A is handed over to the server B, AVF ai AVF value representing participant a's ith data;
2) The encrypted data set of party a is represented by:
Figure FDA0001983086530000027
Figure FDA0001983086530000028
wherein, X 10 An encrypted data set, X, representing a party A held by a server A 11 An encrypted data set representing party A held by server B, i ∈ [1, N ]]，j∈[1，M]，M，N∈N + ；
3) Encrypting data set D of participant B using Yao's encryption algorithm in ABY algorithm using secure multi-party computation 2 And (3) encryption:
Figure FDA0001983086530000029
wherein,
Figure FDA00019830865300000210
a section indicating that the encrypted data set is handed to the server a,
Figure FDA00019830865300000211
representing the part of the encrypted data set handed to server B, enc representing the Yao's encryption algorithm, D 2 A data set representing party B;
each element is encrypted specifically according to the following equation:
Figure FDA00019830865300000212
Figure FDA0001983086530000031
wherein,
Figure FDA0001983086530000032
a section showing that the encrypted data is handed to the server a,
Figure FDA0001983086530000033
a section showing that the encrypted data is handed to the server B, B kj Arbitrary data representing party a;
Figure FDA0001983086530000034
the AVF value representing the encrypted kth piece of data of party B is handed over to part of server a,
Figure FDA0001983086530000035
part of server B that gives AVF value of encrypted kth data of party B to bk AVF value representing the kth piece of data for party B;
4) The encrypted data set of party B is represented by:
Figure FDA0001983086530000036
Figure FDA0001983086530000037
wherein, X 20 An encrypted data set, X, representing a party B held by a server A 21 An encrypted data set representing party B held by Server B, k ∈ [1]，j∈[1，M]，M，P∈N + ；
5) And the participant A and the participant B respectively upload the encrypted data to corresponding servers.
4. The method for cleaning data anomaly points based on the secure multi-party computing technology as claimed in claim 1, wherein the third step of cleaning the data anomaly points of the encrypted data sets uploaded by the participants by the server a and the server B specifically comprises:
1) The server A extracts the last one-dimensional data in the encrypted data set of the party A taken by the server A:
Figure FDA0001983086530000038
server A uses the sorting algorithm in Yao's encryption algorithm in security encryption algorithm ABY to pair A 10 And (4) sorting:
A′ 10 ＝Sort(A 10 )；
wherein A is 10 Last dimension data, A 'in the encrypted dataset representing party A owned by Server A' 10 Represents A' 10 Sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm;
with A 10 Taking X as a reference 10 Also sorted simultaneously, i.e. according to A 10 Descending order of X 10 And after the sorting is finished:
Figure FDA0001983086530000041
wherein, X' 10 Is represented by X 10 Last one-dimensional data, i.e. A 10 The data set submitted to the server A by the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 10 The data in (1) is compared with Thre in order:
Res i ＝Comp(A′ 10i ，Thre)；
wherein, A' 10i Represents A' 10 Element of (1) or (N)]，N∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithms i Is represented by A' 10i If Res is the result of comparison with Thre i Value is 1, represents A' 10i More than or equal to Thre; if Res i Value is 0 and represents A' 10i < Thre, A' 10 The data in (1) is compared with Thre in sequence until Res i =0, stop comparison, compare X' 10 First i row data retention in (1):
Figure FDA0001983086530000042
where I = I, is the first I row of data retained after sorting, j ∈ [1,M]，M∈N + ，X″ 10 Cleaning the final data and then completing the data set of the participant A owned by the server A;
2) The server A extracts the last one-dimensional data in the encrypted data set of the party B taken by the server A:
Figure FDA0001983086530000051
server A uses the sorting Algorithm of the Yao's encryption Algorithm of the secure encryption Algorithm ABY to pair A' 20 And (3) sequencing:
A′ 20 ＝Sort(A 20 )；
wherein A is 20 Last-dimensional data, A 'in encrypted data set representing party B owned by Server A' 20 Is represented by A 20 Sorting the finished data in a descending order, wherein the Sore () represents a sorting algorithm in the Yao's encryption algorithm;
with A 20 Taking X as a reference 20 Also sorted simultaneously, i.e. according to A 20 Descending order of X 20 And after the sorting is finished:
Figure FDA0001983086530000052
wherein, X' 20 Is represented by X 20 Last one-dimensional data, i.e. A 20 Submitting the data set of the server A for the participant B after the reference descending sorting is completed, wherein k belongs to [1]，j∈[1，M]，M，P∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 20 The data in (1), comparing size to Thre in order:
Res k ＝Comp(A′ 20k ，Thre)；
wherein, A' 20k Represents A' 20 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm k Represents A' 20k If Res is the result of comparison with Thre k Value is 1 and represents A' 20k More than or equal to Thre; if Res k Value is 0 and represents A' 20k < Thre, A' 20 The data in (1) is compared with Thre in sequence until Res k =0, stop comparison, compare X' 20 First k rows of data retention:
Figure FDA0001983086530000053
where K = K, is the top reserved after sortingk lines of data, j ∈ [1, M ]]，M∈N + ，X″ 20 The data set of the participant B owned by the server A after the final data cleaning is finished;
3) The server B extracts the last one-dimensional data in the encrypted data set of the party A taken by the server B:
Figure FDA0001983086530000061
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair a 11 And (3) sequencing:
A′ 11 ＝Sort(A 11 )；
wherein A is 11 Last dimension data, A 'in the encrypted dataset representing party A owned by Server B' 11 Is represented by A 11 Sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm;
with A 11 Taking X as a reference 11 Also sorted simultaneously, i.e. according to A 11 Descending order of X 11 After the sorting is finished:
Figure FDA0001983086530000062
wherein, A' 11 Is represented by X 11 Last one-dimensional data, i.e. A 11 The data set submitted to the server B by the participant A after the reference descending sorting is completed, i belongs to [1, N ]]，j∈[1，M]，M，N∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 11 The data in (1) is compared with Thre in order:
Res i ＝Comp(A′ 11i ，Thre)；
wherein, A' 11i Is represented by A' 11 Element of (1, N), i ∈ [1, N ]]，N∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithms i Is represented by A' 11i If Res is the result of comparison with Thre i Value is 1, represents A' 11i More than or equal to Thre; if Res i Value is 0 and represents A' 11i < Thre, A' 11 The data in (1) is compared with Thre in sequence until Res i =0, stop comparison, X' 11 First i row data retention in (1):
Figure FDA0001983086530000071
where I = I, j ∈ [1,M ] for the first I row of data retained after sorting]，M∈N + ，X″ 11 The data set of the participant A owned by the server B after the final data cleaning is finished;
4) The server B extracts the last one-dimensional data in the encrypted data set of the party B taken by the server B:
Figure FDA0001983086530000072
server B uses the sorting algorithm in Yao's encryption algorithm in secure encryption algorithm ABY to pair A 21 And (4) sorting:
A′ 21 ＝Sort(A 21 )；
wherein, A' 21 Last-dimensional data, A 'in encrypted data set representing party B owned by server B' 21 Is shown as A 21 Sorting the finished data in a descending order, wherein the Sort () represents a sorting algorithm in the Yao's encryption algorithm;
with A 21 Taking X as a reference 21 Also sorted simultaneously, i.e. according to A 21 Descending order of X 21 And after the sorting is finished:
Figure FDA0001983086530000073
wherein, X' 21 Is represented by X 21 Last one-dimensional data, i.e. A 21 Submitting the data set of the server B for the participant B after the reference descending sorting is completed, wherein k belongs to [1]，j∈[1，M]，M，P∈N + ；
A 'is defined as a fixed value Thre representing a threshold value of AVF value within a normal range' 21 The data in (1), comparing size to Thre in order:
Res k ＝Comp(A′ 21k ，Thre)；
wherein, A' 21k Is represented by A' 21 The element in (1, P) k ∈]，P∈N + Comp () denotes the comparative size algorithm, res, in Yao's encryption algorithm k Represents A' 21k If Res is the result of comparison with Thre k Value is 1 and represents A' 21k More than or equal to Thre; if Res k Value is 0 and represents A' 21k < Thre, A' 21 The data in (1) is compared with Thre in sequence until Res k =0, stop comparison, X' 21 First k rows of data retention:
Figure FDA0001983086530000081
where K = K, is the first K rows of data retained after sorting, j ∈ [1,M]，M∈M + ，X″ 21 The data set of the participant B owned by the server B after the final data cleaning is finished;
5) The final X ″) 10 ，X″ 11 ，X″ 20 ，X″ 21 And cleaning the finished data set for the final data.
5. A machine learning system applying the method for cleaning abnormal data points based on the secure multi-party computing technology as claimed in any one of claims 1 to 4.
CN201910156492.6A 2019-03-01 2019-03-01 Data anomaly point cleaning method based on safe multi-party computing technology Active CN109992977B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910156492.6A CN109992977B (en) 2019-03-01 2019-03-01 Data anomaly point cleaning method based on safe multi-party computing technology

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910156492.6A CN109992977B (en) 2019-03-01 2019-03-01 Data anomaly point cleaning method based on safe multi-party computing technology

Publications (2)

Publication Number Publication Date
CN109992977A CN109992977A (en) 2019-07-09
CN109992977B true CN109992977B (en) 2022-12-16

Family

ID=67130167

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910156492.6A Active CN109992977B (en) 2019-03-01 2019-03-01 Data anomaly point cleaning method based on safe multi-party computing technology

Country Status (1)

Country Link
CN (1) CN109992977B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111046409B (en) * 2019-12-16 2021-04-13 支付宝(杭州)信息技术有限公司 Private data multi-party security calculation method and system
CN111125735B (en) * 2019-12-20 2021-11-02 支付宝(杭州)信息技术有限公司 Method and system for model training based on private data

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013102488A1 (en) * 2012-01-02 2013-07-11 Telecom Italia S.P.A. Method and system for comparing images
CN108712260A (en) * 2018-05-09 2018-10-26 曲阜师范大学 The multi-party deep learning of privacy is protected to calculate Proxy Method under cloud environment
CN108809628A (en) * 2018-06-13 2018-11-13 哈尔滨工业大学深圳研究生院 Based on the time series method for detecting abnormality and system under Secure

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013102488A1 (en) * 2012-01-02 2013-07-11 Telecom Italia S.P.A. Method and system for comparing images
CN108712260A (en) * 2018-05-09 2018-10-26 曲阜师范大学 The multi-party deep learning of privacy is protected to calculate Proxy Method under cloud environment
CN108809628A (en) * 2018-06-13 2018-11-13 哈尔滨工业大学深圳研究生院 Based on the time series method for detecting abnormality and system under Secure

Also Published As

Publication number Publication date
CN109992977A (en) 2019-07-09

Similar Documents

Publication Publication Date Title
CN110557245B (en) Method and system for SPDZ fault tolerant and secure multiparty computing
CN112906044B (en) Multi-party security calculation method, device, equipment and storage medium
CN107196926A (en) A kind of cloud outsourcing privacy set comparative approach and device
Fan et al. Identity Management Security Authentication Based on Blockchain Technologies.
CN113518092A (en) Set intersection method for realizing multi-party privacy
CN109992977B (en) Data anomaly point cleaning method based on safe multi-party computing technology
CN114548418A (en) Secret sharing-based transverse federal IV algorithm
Li et al. Privacy-aware secure anonymous communication protocol in CPSS cloud computing
CN114614983B (en) Feature fusion privacy protection method based on secure multiparty calculation
CN114614970A (en) Privacy data security processing method based on multi-calculator and homomorphic encryption
CN115664629A (en) Homomorphic encryption-based data privacy protection method for intelligent Internet of things platform
CN109409111B (en) Encrypted image-oriented fuzzy search method
Hsu et al. Private data preprocessing for privacy-preserving Federated Learning
CN115276986B (en) Cloud agent pool shunting re-encryption sharing method under general scene
CN114866312B (en) Shared data determining method and device for protecting data privacy
Wang et al. General survey on massive data encryption
Feng et al. Secure outsourced principal eigentensor computation for cyber-physical-social systems
Liu et al. Power grid data sharing technology based on communication data fusion
CN110943833B (en) Quantum trust model construction method and computer readable storage medium
CN116028969B (en) Privacy calculation method based on data encryption technology
CN116582341B (en) Abnormality detection method, abnormality detection device, abnormality detection apparatus, and storage medium
CN114257412B (en) Privacy protection multi-party data cooperation box-separating method, system, equipment and terminal
Zhao et al. An Efficient Privacy-Preserving Data Aggregation Scheme without Trusted Authority in Smart Grid
CN113132102B (en) Quantum key protection method, device and system based on three layers of keys
CN116471091A (en) Block chain enabled medical internet of things multi-keyword searchable encryption method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant