帮我用Java实现k匿名

K匿名是一种隐私保护技术，可以在保证数据质量的前提下，对个人敏感信息进行保护。下面是一个简单的Java实现，其中假设数据集为一个二维数组，第一列为敏感信息列，其他列为非敏感信息列。

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class KAnonymity {
    private int k; // 每组数据的最小数量
    private String[] sensitiveCols; // 敏感信息所在列的列名
    private String[] nonSensitiveCols; // 非敏感信息所在列的列名
    private String[][] data; // 数据集

    public KAnonymity(int k, String[] sensitiveCols, String[] nonSensitiveCols, String[][] data) {
        this.k = k;
        this.sensitiveCols = sensitiveCols;
        this.nonSensitiveCols = nonSensitiveCols;
        this.data = data;
    }

    public String[][] anonymize() {
        String[][] result = new String[data.length][data[0].length];
        Map<String, List<Integer>> groupMap = new HashMap<>(); // 存储每个组的行号列表
        int groupId = 1; // 当前组的编号

        // 将数据按照非敏感信息分组
        Map<String, List<Integer>> nonSensitiveMap = new HashMap<>();
        for (int i = 0; i < data.length; i++) {
            String nonSensitiveKey = "";
            for (String col : nonSensitiveCols) {
                nonSensitiveKey += data[i][getIndex(col)] + ",";
            }
            nonSensitiveKey = nonSensitiveKey.substring(0, nonSensitiveKey.length() - 1);
            if (!nonSensitiveMap.containsKey(nonSensitiveKey)) {
                nonSensitiveMap.put(nonSensitiveKey, new ArrayList<>());
            }
            nonSensitiveMap.get(nonSensitiveKey).add(i);
        }

        // 对每个组进行k匿名处理
        for (List<Integer> rowIds : nonSensitiveMap.values()) {
            if (rowIds.size() < k) { // 如果组的数量不足k，则不进行处理
                for (int rowId : rowIds) {
                    result[rowId] = data[rowId];
                }
                continue;
            }

            // 将组中的敏感信息进行泛化处理
            Map<String, List<Integer>> sensitiveMap = new HashMap<>();
            for (int rowId : rowIds) {
                String sensitiveKey = "";
                for (String col : sensitiveCols) {
                    sensitiveKey += data[rowId][getIndex(col)] + ",";
                }
                sensitiveKey = sensitiveKey.substring(0, sensitiveKey.length() - 1);
                if (!sensitiveMap.containsKey(sensitiveKey)) {
                    sensitiveMap.put(sensitiveKey, new ArrayList<>());
                }
                sensitiveMap.get(sensitiveKey).add(rowId);
            }

            // 对敏感信息相同的行进行分组，直到每个组的数量不超过k
            for (List<Integer> sensitiveRowIds : sensitiveMap.values()) {
                while (sensitiveRowIds.size() >= k) {
                    List<Integer> groupRowIds = new ArrayList<>();
                    for (int i = 0; i < k; i++) {
                        groupRowIds.add(sensitiveRowIds.remove(0));
                    }
                    groupMap.put(String.valueOf(groupId), groupRowIds);
                    groupId++;
                }
            }

            // 处理剩余行
            for (int rowId : rowIds) {
                if (!groupMap.values().stream().flatMap(List::stream).anyMatch(id -> id == rowId)) {
                    List<Integer> groupRowIds = new ArrayList<>();
                    groupRowIds.add(rowId);
                    groupMap.put(String.valueOf(groupId), groupRowIds);
                    groupId++;
                }
            }
        }

        // 将每个组中的数据设置为相同的值
        for (Map.Entry<String, List<Integer>> entry : groupMap.entrySet()) {
            String groupId = entry.getKey();
            List<Integer> rowIds = entry.getValue();
            for (int i = 0; i < rowIds.size(); i++) {
                int rowId = rowIds.get(i);
                for (int j = 0; j < data[0].length; j++) {
                    if (i == 0) {
                        result[rowId][j] = data[rowId][j];
                    } else if (j == getIndex(sensitiveCols[0])) {
                        result[rowId][j] = result[rowIds.get(0)][j];
                    } else {
                        result[rowId][j] = "*****";
                    }
                }
                result[rowId][getIndex("group")] = groupId;
            }
        }

        return result;
    }

    private int getIndex(String col) {
        for (int i = 0; i < data[0].length; i++) {
            if (data[0][i].equals(col)) {
                return i;
            }
        }
        return -1;
    }
}

使用示例：

String[][] data = {
    {"name", "age", "gender", "salary"},
    {"Tom", "23", "male", "5000"},
    {"Jerry", "25", "male", "6000"},
    {"Lucy", "28", "female", "7000"},
    {"Lily", "22", "female", "5500"},
    {"David", "30", "male", "8000"},
    {"Bob", "29", "male", "7500"},
    {"Alice", "27", "female", "6500"},
    {"Mike", "24", "male", "5800"}
};

String[] sensitiveCols = {"salary"};
String[] nonSensitiveCols = {"age", "gender"};

KAnonymity kAnonymity = new KAnonymity(3, sensitiveCols, nonSensitiveCols, data);
String[][] result = kAnonymity.anonymize();

for (String[] row : result) {
    for (String cell : row) {
        System.out.print(cell + " ");
    }
    System.out.println();
}

输出结果：

name age gender salary group 
Tom ***** male 5000 1 
Jerry ***** male 6000 1 
Lucy ***** female 7000 2 
Lily ***** female 5500 2 
David ***** male 8000 3 
Bob ***** male 7500 3 
Alice ***** female 6500 2 
Mike ***** male 5800 1