用C语言实现对Adult Data Set数据集进行智能k匿名处理

实现对Adult Data Set数据集进行智能k匿名处理，可以按照以下步骤进行：

1. 读取Adult Data Set数据集，将其存储在内存中。

2. 将数据集中的每个属性进行分类，分为敏感属性和非敏感属性。

3. 对敏感属性进行k匿名处理，确保每个属性值至少有k个相同的值。

4. 对非敏感属性进行一般化处理，将每个属性值映射到一个较小的集合中。

5. 合并处理后的敏感和非敏感属性，并输出最终的数据集。

以下是一个用C语言实现对Adult Data Set数据集进行智能k匿名处理的示例代码：

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_LINE_LEN 1024

struct record {
    char age[16];
    char workclass[32];
    char fnlwgt[16];
    char education[32];
    char education_num[16];
    char marital_status[32];
    char occupation[32];
    char relationship[32];
    char race[16];
    char sex[16];
    char capital_gain[16];
    char capital_loss[16];
    char hours_per_week[16];
    char native_country[32];
};

struct anonymous_record {
    char age[16];
    char workclass[32];
    char fnlwgt[16];
    char education[32];
    char education_num[16];
    char marital_status[32];
    char occupation[32];
    char relationship[32];
    char race[16];
    char sex[16];
    char capital_gain[16];
    char capital_loss[16];
    char hours_per_week[16];
    char native_country[32];
    char sensitive_attribute[32];
};

void k_anonymity(struct record *data, int num_records, int k);
void generalization(struct record *data, int num_records);

int main()
{
    struct record data[MAX_LINE_LEN];
    struct anonymous_record anon_data[MAX_LINE_LEN];
    int num_records = 0;
    int k = 5;

    FILE *fp = fopen("adult.data", "r");
    if (fp == NULL) {
        printf("Failed to open file!\n");
        return 1;
    }

    char line[MAX_LINE_LEN];
    while (fgets(line, MAX_LINE_LEN, fp) != NULL) {
        sscanf(line, "%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^,],%[^\n]",
               data[num_records].age, data[num_records].workclass, data[num_records].fnlwgt,
               data[num_records].education, data[num_records].education_num, data[num_records].marital_status,
               data[num_records].occupation, data[num_records].relationship, data[num_records].race,
               data[num_records].sex, data[num_records].capital_gain, data[num_records].capital_loss,
               data[num_records].hours_per_week, data[num_records].native_country);
        num_records++;
    }

    fclose(fp);

    generalization(data, num_records);
    k_anonymity(data, num_records, k);

    for (int i = 0; i < num_records; i++) {
        strcpy(anon_data[i].age, data[i].age);
        strcpy(anon_data[i].workclass, data[i].workclass);
        strcpy(anon_data[i].fnlwgt, data[i].fnlwgt);
        strcpy(anon_data[i].education, data[i].education);
        strcpy(anon_data[i].education_num, data[i].education_num);
        strcpy(anon_data[i].marital_status, data[i].marital_status);
        strcpy(anon_data[i].occupation, data[i].occupation);
        strcpy(anon_data[i].relationship, data[i].relationship);
        strcpy(anon_data[i].race, data[i].race);
        strcpy(anon_data[i].sex, data[i].sex);
        strcpy(anon_data[i].capital_gain, data[i].capital_gain);
        strcpy(anon_data[i].capital_loss, data[i].capital_loss);
        strcpy(anon_data[i].hours_per_week, data[i].hours_per_week);
        strcpy(anon_data[i].native_country, data[i].native_country);
        strcpy(anon_data[i].sensitive_attribute, data[i].marital_status); // 随便选一个敏感属性
    }

    // 输出最终的数据集
    for (int i = 0; i < num_records; i++) {
        printf("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n",
               anon_data[i].age, anon_data[i].workclass, anon_data[i].fnlwgt,
               anon_data[i].education, anon_data[i].education_num, anon_data[i].marital_status,
               anon_data[i].occupation, anon_data[i].relationship, anon_data[i].race,
               anon_data[i].sex, anon_data[i].capital_gain, anon_data[i].capital_loss,
               anon_data[i].hours_per_week, anon_data[i].native_country);
    }

    return 0;
}

void k_anonymity(struct record *data, int num_records, int k)
{
    // 对敏感属性进行k匿名处理
    for (int i = 0; i < num_records; i++) {
        int count = 0;
        for (int j = 0; j < num_records; j++) {
            if (strcmp(data[i].marital_status, data[j].marital_status) == 0) {
                count++;
            }
        }
        if (count < k) {
            // 找到一个和当前记录的敏感属性值不同的记录，将其敏感属性值替换为当前记录的敏感属性值
            for (int j = 0; j < num_records; j++) {
                if (strcmp(data[i].marital_status, data[j].marital_status) != 0) {
                    strcpy(data[j].marital_status, data[i].marital_status);
                    break;
                }
            }
        }
    }
}

void generalization(struct record *data, int num_records)
{
    // 对非敏感属性进行一般化处理
    for (int i = 0; i < num_records; i++) {
        // 年龄一般化
        if (atoi(data[i].age) < 25) {
            strcpy(data[i].age, "under-25");
        } else if (atoi(data[i].age) < 45) {
            strcpy(data[i].age, "25-44");
        } else if (atoi(data[i].age) < 65) {
            strcpy(data[i].age, "45-64");
        } else {
            strcpy(data[i].age, "65+");
        }

        // 工作类型一般化
        if (strcmp(data[i].workclass, "Private") == 0) {
            strcpy(data[i].workclass, "Non-government");
        } else {
            strcpy(data[i].workclass, "Government");
        }

        // 学历一般化
        if (strcmp(data[i].education, "Bachelors") == 0 || strcmp(data[i].education, "Masters") == 0 ||
            strcmp(data[i].education, "Doctorate") == 0) {
            strcpy(data[i].education, "Higher-Education");
        } else {
            strcpy(data[i].education, "Lower-Education");
        }

        // 工作时长一般化
        if (atoi(data[i].hours_per_week) < 20) {
            strcpy(data[i].hours_per_week, "<20");
        } else if (atoi(data[i].hours_per_week) < 40) {
            strcpy(data[i].hours_per_week, "20-39");
        } else {
            strcpy(data[i].hours_per_week, ">=40");
        }
    }
}

注意：这只是一个简单的示例代码，实际应用中还需要考虑更多因素，例如如何选择敏感属性、如何进行一般化处理等。同时，为了保护隐私，应该在处理后的数据集中删除原始数据，只保留处理后的数据。