差分隐私java_差分隐私-Laplace机制（后附指数和laplace的java和python实现）

_salary DECIMAL(10,2);
DECLARE allowance DECIMAL(10,2);
SELECT s.basic_salary, s.post_salary, s差分隐私是一种在保护数据隐私的同时，允许对数据进行分析的技术。Lap.allowance INTO basic_salary, post_salary, allowance
FROM salary s
WHERE s.employee_id = employee_id;
RETURN basiclace机制是差分隐私中常用的一种噪声添加方法，可以在查询中添加噪声以保护_salary + post_salary + allowance;
END;

4. 计算部门的平均工资的存储函数

CREATE FUNCTION `get_department_avg_salary`(department_id INT) RETURNS DECIMAL(10,2)
BEGIN
DECLARE total_salary DECIMAL查询结果的隐私。

Java实现Laplace机制：

import java.util.Random;

public class LaplaceMechanism(10,2);
    DECLARE employee_count INT;
    SELECT SUM(s.basic_salary + s.post_salary + s.allowance), COUNT(e.id {
    /**
     * @param epsilon 隐私预算
     * @param sensitivity 敏感度
     * @param value 原始) INTO total_salary, employee_count
    FROM employee e
    LEFT JOIN salary s ON e.id = s.employee_id
    WHERE e查询结果
     * @return 添加拉普拉斯噪声后的查询结果
     */
    public static double addNoise(double epsilon.department_id = department_id;
    RETURN total_salary / employee_count;
END;

5. 查询所有职工的总工资, double sensitivity, double value) {
Random rand = new Random();
double u = rand.nextDouble() - 0.5;
double laplaceNoise = -sensitivity / epsilon * Math.signum(u) * Math.log(1 - 2 * Math.abs(u));
和平均工资的存储过程

CREATE PROCEDURE `get_employee_salary_statistic`()
BEGIN
    SELECT SUM        return value + laplaceNoise;
    }
}

Python实现Laplace机制：

import random
import(s.basic_salary + s.post_salary + s.allowance) AS total_salary, AVG(s.basic_salary + s.post_salary + s.allowance) AS avg_salary
    FROM salary s;
END;