Threshold-based homomorphic encryption privacy protection device and method
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A technology of homomorphic encryption and privacy protection, applied in the field of threshold-based homomorphic encryption privacy protection devices, to achieve the effect of improving security
Active Publication Date: 2022-01-14
BEIJING ZHONGKE JINCAI TECH
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But if you want to operate on the data again, you must decrypt it
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Embodiment 1
[0029] Such as figure 1 As shown, the present application provides a threshold-based homomorphic encryption privacy protection method, which includes the following steps:
[0030] Step S0, generating private data in advance.
[0031] The user terminal generates the user's private data, and indicates the date of generation of the private data.
[0032] Step S1, according to the pre-built sentence classification model for confidentiality levels, divide the private data into multiple data fragment groups according to the different confidentiality levels, and mark the sequence number of each data fragment.
[0033] Wherein, the data fragment group includes multiple data fragments with the same confidentiality level, and the sequence numbers of the data fragments indicate the position sequence of the data fragments in the private data, and the private data can be restored by sorting the disordered multiple data fragments by the sequence numbers.
[0034] Such as figure 2 As sho...
Embodiment 2
[0091] Such as Figure 6 As shown, the present application provides a threshold-based homomorphic encryption privacy protection device 100, which device includes:
[0092] The data segment division unit 10 is used to divide the private data into a plurality of data segment groups according to the different confidentiality levels according to the pre-built sentence secrecy level division model;
[0093] An encryption processor 20, configured to perform separate encryption processing on each data segment group;
[0094] A verification data acquisition unit 30, configured to acquire verification data corresponding to each data segment group in response to an instruction to acquire private data;
[0095] A verification processor 40, configured to verify the acquired verification data;
[0096] The collection module 50 is used to collect intrusion attribute characteristic data in the verification data verification process in real time;
[0097] A data processor 60, configured to...
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Abstract
The invention provides a threshold-based homomorphic encryption privacy protection device and method, and the method comprises the following steps: dividing privacy data into a plurality of data segment groups according to different secrecy levels according to a pre-constructed statement secrecy level division model; performing independent encryption processing on each data segment group; in response to an instruction for acquiring the private data, acquiring verification data corresponding to each data segment group; verifying the obtained verification data; acquiring intrusion attribute characteristic data in a verification process of the verification data in real time; calculating an intrusion risk value based on the intrusion attribute data; and comparing the intrusion risk value with a preset intrusion tolerance limit value, if the intrusion risk value is greater than the preset intrusion tolerance limit value, forbidding to acquire the data of the data segment group, otherwise, allowing to acquire the data of the data segment group. According to the method, the security of the privacy data is improved, and the privacy data is prevented from being stolen by an intruder.
Description
technical field [0001] The present application relates to the technical field of data processing, in particular to a threshold-based homomorphic encryption privacy protection device and method. Background technique [0002] Homomorphic Encryption (HE) refers to an encryption algorithm that satisfies the properties of cryptographic homomorphic operations, that is, after data undergoes homomorphic encryption, a specific calculation is performed on the password, and the obtained password calculation result is obtained after corresponding homomorphic decryption. The plaintext is equivalent to directly performing the same calculation on the plaintext data, realizing the "computable and invisible" of the data. [0003] It is common to encrypt stored data and ensure that only encrypted data is transferred to cloud storage devices. But if you want to manipulate the data again, you must decrypt it. This provides an opportunity for intruders lurking in the network to steal decrypted...
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Application Information
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