帮我用Java写一个实现k匿名的代码

好的，这里给你一个简单的Java代码实现K匿名算法，供你参考：

import java.util.*;

public class KAnonymity {
    
    // 用于存储数据集的列表
    private List<Map<String, String>> dataset;
    
    // 敏感性属性的名称
    private String sensitiveAttribute;
    
    // 敏感性属性值的敏感性等级
    private Map<String, Integer> sensitivityLevels;
    
    // K值
    private int k;
    
    /**
     * 构造函数，初始化数据集、敏感性属性、敏感性等级和K值
     */
    public KAnonymity(List<Map<String, String>> dataset, String sensitiveAttribute, Map<String, Integer> sensitivityLevels, int k) {
        this.dataset = dataset;
        this.sensitiveAttribute = sensitiveAttribute;
        this.sensitivityLevels = sensitivityLevels;
        this.k = k;
    }
    
    /**
     * 实现K匿名算法
     */
    public List<Map<String, String>> anonymize() {
        List<Map<String, String>> result = new ArrayList<>();
        
        // 循环直到所有记录都被匿名化
        while (!dataset.isEmpty()) {
            // 筛选出所有未被匿名化的记录
            List<Map<String, String>> unanonymizedRecords = getUnanonymizedRecords();
            if (unanonymizedRecords.isEmpty()) {
                break;
            }
            
            // 按照敏感性级别进行排序
            Collections.sort(unanonymizedRecords, new RecordComparator(sensitiveAttribute, sensitivityLevels));
            
            // 对于每个等级，将记录按照该属性值进行分组
            Map<String, List<Map<String, String>>> groups = new HashMap<>();
            for (Map<String, String> record : unanonymizedRecords) {
                String sensitiveValue = record.get(sensitiveAttribute);
                List<Map<String, String>> group = groups.get(sensitiveValue);
                if (group == null) {
                    group = new ArrayList<>();
                    groups.put(sensitiveValue, group);
                }
                group.add(record);
            }
            
            // 对于每个分组，检查是否满足K匿名条件，如果不满足，则进行匿名化操作
            for (List<Map<String, String>> group : groups.values()) {
                if (group.size() < k) {
                    // 如果分组大小小于K值，则将所有记录通用化敏感属性值
                    for (Map<String, String> record : group) {
                        record.put(sensitiveAttribute, "*");
                    }
                    result.addAll(group);
                } else {
                    // 如果分组大小大于等于K值，则随机生成虚假值
                    String sensitiveValue = group.get(0).get(sensitiveAttribute);
                    List<String> fakeValues = generateFakeValues(sensitiveValue, sensitivityLevels.get(sensitiveValue), k);
                    for (int i = 0; i < group.size(); i++) {
                        Map<String, String> record = group.get(i);
                        record.put(sensitiveAttribute, fakeValues.get(i));
                    }
                    result.addAll(group);
                }
                
                // 将已匿名化的记录从原始数据集中删除
                dataset.removeAll(group);
            }
        }
        
        return result;
    }
    
    /**
     * 获取所有未被匿名化的记录
     */
    private List<Map<String, String>> getUnanonymizedRecords() {
        List<Map<String, String>> unanonymizedRecords = new ArrayList<>();
        for (Map<String, String> record : dataset) {
            if (!"*".equals(record.get(sensitiveAttribute))) {
                unanonymizedRecords.add(record);
            }
        }
        return unanonymizedRecords;
    }
    
    /**
     * 随机生成虚假值
     */
    private List<String> generateFakeValues(String sensitiveValue, int sensitivityLevel, int count) {
        List<String> fakeValues = new ArrayList<>();
        Random random = new Random();
        for (int i = 0; i < count; i++) {
            fakeValues.add(generateFakeValue(sensitiveValue, sensitivityLevel, random));
        }
        return fakeValues;
    }
    
    /**
     * 随机生成单个虚假值
     */
    private String generateFakeValue(String sensitiveValue, int sensitivityLevel, Random random) {
        int sensitivityRange = (int) Math.pow(10, sensitivityLevel);
        int minValue = Integer.parseInt(sensitiveValue) / sensitivityRange * sensitivityRange;
        int maxValue = minValue + sensitivityRange - 1;
        return Integer.toString(minValue + random.nextInt(sensitivityRange));
    }
    
    /**
     * 用于比较记录的类，按照指定属性的敏感性等级进行排序
     */
    private class RecordComparator implements Comparator<Map<String, String>> {
        
        private String sensitiveAttribute;
        private Map<String, Integer> sensitivityLevels;
        
        public RecordComparator(String sensitiveAttribute, Map<String, Integer> sensitivityLevels) {
            this.sensitiveAttribute = sensitiveAttribute;
            this.sensitivityLevels = sensitivityLevels;
        }
        
        @Override
        public int compare(Map<String, String> o1, Map<String, String> o2) {
            int level1 = sensitivityLevels.get(o1.get(sensitiveAttribute));
            int level2 = sensitivityLevels.get(o2.get(sensitiveAttribute));
            return Integer.compare(level2, level1);
        }
    }
}

使用方法：

// 测试数据集
List<Map<String, String>> dataset = new ArrayList<>();
Map<String, String> record1 = new HashMap<>();
record1.put("name", "Alice");
record1.put("age", "20");
record1.put("zip", "12345");
record1.put("gender", "F");
record1.put("salary", "50000");
dataset.add(record1);
Map<String, String> record2 = new HashMap<>();
record2.put("name", "Bob");
record2.put("age", "25");
record2.put("zip", "67890");
record2.put("gender", "M");
record2.put("salary", "60000");
dataset.add(record2);
Map<String, String> record3 = new HashMap<>();
record3.put("name", "Carol");
record3.put("age", "30");
record3.put("zip", "12345");
record3.put("gender", "F");
record3.put("salary", "70000");
dataset.add(record3);

// 敏感性属性和敏感性等级
String sensitiveAttribute = "salary";
Map<String, Integer> sensitivityLevels = new HashMap<>();
sensitivityLevels.put("50000", 1);
sensitivityLevels.put("60000", 2);
sensitivityLevels.put("70000", 3);

// K值
int k = 2;

// 创建KAnonymity对象并执行匿名化
KAnonymity anonymizer = new KAnonymity(dataset, sensitiveAttribute, sensitivityLevels, k);
List<Map<String, String>> result = anonymizer.anonymize();

// 输出匿名化结果
for (Map<String, String> record : result) {
    System.out.println(record);
}

这里的数据集包含了姓名、年龄、邮编、性别和工资等属性，其中工资是敏感性属性，其敏感性等级由敏感性值决定。K值是2，表示每个分组的大小必须大于等于2。在执行匿名化之后，输出匿名化结果。