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Method for protecting privacy in federated learning prediction stage based on PSI technology
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Active Publication Date: 2020-06-09
百融云创科技股份有限公司
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Although PSI technology is relatively mature, there is no relevant research on how to use it in the prediction stage of federated learning
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Embodiment 1
[0032] Let the machine learning algorithm used in the present invention be logistic regression, the data feature of Alice is Bob's data features are where d A and d B are the number of features owned by Alice and Bob respectively. The partial models obtained by Alice and Bob through federated learning are respectively with where θ -1 is the intercept. The prediction function of logistic regression is in For specific sample data, the first half Owned by Alice, second half owned by Bob. Then Alice performs the prediction according to the following steps:
[0033] 1. For all id∈ID B , Bob calculates Form a new data set and record it as Predict B ={(id,score B,id )|id∈ID B};
[0034] 2. Alice and Bob execute the improved OT protocol-based PSI to obtain new data set eID respectively A ={eid A,id |id∈ID A} and eP B ={(eid B,id ,score B,id )|id∈ID B};
[0043] Same as the symbol used in Embodiment 1, the prediction is also made using logistic regression, the only difference is that the PSI based on the public key is used in this embodiment. For this reason, some other symbols need to be agreed upon. Assume that this implementation case is based on elliptic curves, and q is the order of elliptic curves. Represents the set {1,2,...,q-1}, H 1 and H 2 is a hash function, where H 1 Messages can be mapped to points on an elliptic curve. Specific steps are as follows:
[0044] 1. Precomputation, for all id∈ID B , Bob randomly chooses calculate s id =H 2 (a*H 1 (id)), and all s id ,id∈ID B send to Alice;
[0045] 2. For all id ∈ ID A , Alice randomly chooses calculate t id =b*H 1 (id), and t id ,id∈ID A send to Bob;
[0046] 3. Bob performs the following steps:
[0047] 3.1. For all id∈ID B , according to Alice's needs, Bob calculates
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Abstract
The invention discloses a method for protecting privacy in a federated learning prediction stage based on a PSI technology. The method comprises the following steps that firstly, a prediction serviceparty calculates a prediction result of a model of the prediction service party, then the prediction service party and the prediction service party execute an improved PSI protocol, a part of model calculation results of the prediction service party are encrypted, a prediction demand party decrypts the calculation result of a data provider in combination with data of the prediction demand party, and finally a prediction result of a keyword id shared by the prediction service party and the prediction demand party is obtained. According to the method, the PSI technology is utilized, the data areencrypted through the key derivation function, the privacy protection requirement in the federated learning prediction stage is met, the last link of federated learning privacy security protection isbroken through, and landing of more application scenes of federated learning is promoted.
Description
technical field [0001] The invention relates to the fields of information security and artificial intelligence, in particular to the application of PSI technology in federated learning to realize privacy protection in the prediction stage of federated learning, and is a method for protecting privacy in the predicted stage of federated learning based on PSI technology. Background technique [0002] The rapid development of emerging technologies such as big data, cloud computing, and the Internet of Things has led to an explosive growth of data, which is in the hands of different entities. On the one hand, with the promulgation of laws and regulations such as my country's "Network Security Law" and the EU's "General Data Protection Regulation" (GDPR), governments of various countries will have stricter requirements for user data privacy protection. Bottlenecks have emerged in the use and analysis of data; on the other hand, various entities that hold a large amount of data hope...
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