User privacy protection method in personalized information retrieval
A technology for information retrieval and user privacy, applied in digital data protection, special data processing applications, instruments, etc., can solve problems such as user privacy leakage, and achieve the effect of improving personalized service performance
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[0010] (1) Hide the identifiers in the user model, and set a reasonable initial value of the privacy budget parameter ε.
[0011] (2) Using a top-down method, the probabilistic generalization quasi-identifier can divide the data set into some equivalence groups.
[0012] (3) Add Lap(2 / ε) noise to each set of data.
[0013] (4) Publish the dataset that satisfies differential privacy.
[0014] (5) Complete the user privacy protection method in personalized information retrieval.
[0015] The detailed description of the user privacy protection method in personalized information retrieval is as follows:
[0016]
[0017] Note:
[0018] ε-differential privacy: Given two data sets D and D′, there is at most one record difference between D and D′, and a privacy algorithm A is given, Range(A) is the value range of A, if the algorithm A is in the data Arbitrary output results on sets D and D′ Satisfy the following inequality, then A satisfies ε-differential privacy, that is, ...
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