【数据安全】敏感字过滤方案总结
1.Trie 树实现方案
2.AC自动机方案
3.DFA算法方案
4.开源方案
1.Trie 树实现方案
Trie 树 也称为字典树、单词查找树，哈希树的一种变种，通常被用于字符串匹配，用来解决在一组字符串集合中快速查找某个字符串的问题
当我们要查找对应的字符串“东京热”的话，我们会把这个字符串切割成单个的字符“东”、“京”、“热”，然后我们从 Trie 树的根节点开始匹配。
Trie 树的核心原理其实很简单，就是通过公共前缀来提高字符串匹配效率
Trie trie = new PatriciaTrie<>();
trie.put("Abigail", "student");
trie.put("Abi", "doctor");
trie.put("Annabel", "teacher");
trie.put("Christina", "student");
trie.put("Chris", "doctor");
Assertions.assertTrue(trie.containsKey("Abigail"));
assertEquals("{Abi=doctor, Abigail=student}", trie.prefixMap("Abi").toString());
assertEquals("{Chris=doctor, Christina=student}", trie.prefixMap("Chr").toString());
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Trie 树是一种利用空间换时间的数据结构，占用的内存会比较大。也正是因为这个原因，实际工程项目中都是使用的改进版 Trie 树例如双数组 Trie 树。相比较于 Trie 树，DAT 的内存占用极低，可以达到 Trie 树内存的 1%左右。DAT 在中文分词、自然语言处理、信息检索等领域有广泛的应用，是一种非常优秀的数据结构
代码如下：
/**
- DoubleArrayTrie: Java implementation of Darts (Double-ARray Trie System)
Copyright(C) 2001-2007 Taku Kudo <taku@chasen.org>
Copyright(C) 2009 MURAWAKI Yugo <murawaki@nlp.kuee.kyoto-u.ac.jp>
- Copyright(C) 2012 KOMIYA Atsushi <komiya.atsushi@gmail.com>
- The contents of this file may be used under the terms of either of the GNU
- Lesser General Public License Version 2.1 or later (the "LGPL"), or the BSD
- License (the "BSD").
*/
package darts;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
public class DoubleArrayTrie {
private final static int BUF_SIZE = 16384;
private final static int UNIT_SIZE = 8; // size of int + int
private static class Node {
int code;
int depth;
int left;
int right;
};
private int check[];
private int base[];
private boolean used[];
private int size;
private int allocSize;
private List<String> key;
private int keySize;
private int length[];
private int value[];
private int progress;
private int nextCheckPos;
// boolean no_delete_;
int error_;
// int (*progressfunc_) (size_t, size_t);
// inline _resize expanded
private int resize(int newSize) {
int[] base2 = new int[newSize];
int[] check2 = new int[newSize];
boolean used2[] = new boolean[newSize];
if (allocSize > 0) {
System.arraycopy(base, 0, base2, 0, allocSize);
System.arraycopy(check, 0, check2, 0, allocSize);
System.arraycopy(used2, 0, used2, 0, allocSize);
}
base = base2;
check = check2;
used = used2;
return allocSize = newSize;
}
private int fetch(Node parent, List<Node> siblings) {
if (error_ < 0)
return 0;
int prev = 0;
for (int i = parent.left; i < parent.right; i++) {
if ((length != null ? length[i] : key.get(i).length()) < parent.depth)
continue;
String tmp = key.get(i);
int cur = 0;
if ((length != null ? length[i] : tmp.length()) != parent.depth)
cur = (int) tmp.charAt(parent.depth) + 1;
if (prev > cur) {
error_ = -3;
return 0;
}
if (cur != prev || siblings.size() == 0) {
Node tmp_node = new Node();
tmp_node.depth = parent.depth + 1;
tmp_node.code = cur;
tmp_node.left = i;
if (siblings.size() != 0)
siblings.get(siblings.size() - 1).right = i;
siblings.add(tmp_node);
}
prev = cur;
}
if (siblings.size() != 0)
siblings.get(siblings.size() - 1).right = parent.right;
return siblings.size();
}
private int insert(List<Node> siblings) {
if (error_ < 0)
return 0;
int begin = 0;
int pos = ((siblings.get(0).code + 1 > nextCheckPos) ? siblings.get(0).code + 1
: nextCheckPos) - 1;
int nonzero_num = 0;
int first = 0;
if (allocSize <= pos)
resize(pos + 1);
outer: while (true) {
pos++;
if (allocSize <= pos)
resize(pos + 1);
if (check[pos] != 0) {
nonzero_num++;
continue;
} else if (first == 0) {
nextCheckPos = pos;
first = 1;
}
begin = pos - siblings.get(0).code;
if (allocSize <= (begin + siblings.get(siblings.size() - 1).code)) {
// progress can be zero
double l = (1.05 > 1.0 * keySize / (progress + 1)) ? 1.05 : 1.0
* keySize / (progress + 1);
resize((int) (allocSize * l));
}
if (used[begin])
continue;
for (int i = 1; i < siblings.size(); i++)
if (check[begin + siblings.get(i).code] != 0)
continue outer;
break;
}
// -- Simple heuristics --
// if the percentage of non-empty contents in check between the
// index
// 'next_check_pos' and 'check' is greater than some constant value
// (e.g. 0.9),
// new 'next_check_pos' index is written by 'check'.
if (1.0 * nonzero_num / (pos - nextCheckPos + 1) >= 0.95)
nextCheckPos = pos;
used[begin] = true;
size = (size > begin + siblings.get(siblings.size() - 1).code + 1) ? size
: begin + siblings.get(siblings.size() - 1).code + 1;
for (int i = 0; i < siblings.size(); i++)
check[begin + siblings.get(i).code] = begin;
for (int i = 0; i < siblings.size(); i++) {
List<Node> new_siblings = new ArrayList<Node>();
if (fetch(siblings.get(i), new_siblings) == 0) {
base[begin + siblings.get(i).code] = (value != null) ? (-value[siblings
.get(i).left] - 1) : (-siblings.get(i).left - 1);
if (value != null && (-value[siblings.get(i).left] - 1) >= 0) {
error_ = -2;
return 0;
}
progress++;
// if (progress_func_) (*progress_func_) (progress,
// keySize);
} else {
int h = insert(new_siblings);
base[begin + siblings.get(i).code] = h;
}
}
return begin;
}
public DoubleArrayTrie() {
check = null;
base = null;
used = null;
size = 0;
allocSize = 0;
// no_delete_ = false;
error_ = 0;
}
// no deconstructor
// set_result omitted
// the search methods returns (the list of) the value(s) instead
// of (the list of) the pair(s) of value(s) and length(s)
// set_array omitted
// array omitted
void clear() {
// if (! no_delete_)
check = null;
base = null;
used = null;
allocSize = 0;
size = 0;
// no_delete_ = false;
}
public int getUnitSize() {
return UNIT_SIZE;
}
public int getSize() {
return size;
}
public int getTotalSize() {
return size * UNIT_SIZE;
}
public int getNonzeroSize() {
int result = 0;
for (int i = 0; i < size; i++)
if (check[i] != 0)
result++;
return result;
}
public int build(List<String> key) {
return build(key, null, null, key.size());
}
public int build(List<String> _key, int _length[], int _value[],
int _keySize) {
if (_keySize > _key.size() || _key == null)
return 0;
// progress_func_ = progress_func;
key = _key;
length = _length;
keySize = _keySize;
value = _value;
progress = 0;
resize(65536 * 32);
base[0] = 1;
nextCheckPos = 0;
Node root_node = new Node();
root_node.left = 0;
root_node.right = keySize;
root_node.depth = 0;
List<Node> siblings = new ArrayList<Node>();
fetch(root_node, siblings);
insert(siblings);
// size += (1 << 8 * 2) + 1; // ???
// if (size >= allocSize) resize (size);
used = null;
key = null;
return error_;
}
public void open(String fileName) throws IOException {
File file = new File(fileName);
size = (int) file.length() / UNIT_SIZE;
check = new int[size];
base = new int[size];
DataInputStream is = null;
try {
is = new DataInputStream(new BufferedInputStream(
new FileInputStream(file), BUF_SIZE));
for (int i = 0; i < size; i++) {
base[i] = is.readInt();
check[i] = is.readInt();
}
} finally {
if (is != null)
is.close();
}
}
public void save(String fileName) throws IOException {
DataOutputStream out = null;
try {
out = new DataOutputStream(new BufferedOutputStream(
new FileOutputStream(fileName)));
for (int i = 0; i < size; i++) {
out.writeInt(base[i]);
out.writeInt(check[i]);
}
out.close();
} finally {
if (out != null)
out.close();
}
}
public int exactMatchSearch(String key) {
return exactMatchSearch(key, 0, 0, 0);
}
public int exactMatchSearch(String key, int pos, int len, int nodePos) {
if (len <= 0)
len = key.length();
if (nodePos <= 0)
nodePos = 0;
int result = -1;
char[] keyChars = key.toCharArray();
int b = base[nodePos];
int p;
for (int i = pos; i < len; i++) {
p = b + (int) (keyChars[i]) + 1;
if (b == check[p])
b = base[p];
else
return result;
}
p = b;
int n = base[p];
if (b == check[p] && n < 0) {
result = -n - 1;
}
return result;
}
public List<Integer> commonPrefixSearch(String key) {
return commonPrefixSearch(key, 0, 0, 0);
}
public List<Integer> commonPrefixSearch(String key, int pos, int len,
int nodePos) {
if (len <= 0)
len = key.length();
if (nodePos <= 0)
nodePos = 0;
List<Integer> result = new ArrayList<Integer>();
char[] keyChars = key.toCharArray();
int b = base[nodePos];
int n;
int p;
for (int i = pos; i < len; i++) {
p = b;
n = base[p];
if (b == check[p] && n < 0) {
result.add(-n - 1);
}
p = b + (int) (keyChars[i]) + 1;
if (b == check[p])
b = base[p];
else
return result;
}
p = b;
n = base[p];
if (b == check[p] && n < 0) {
result.add(-n - 1);
}
return result;
}
// debug
public void dump() {
for (int i = 0; i < size; i++) {
System.err.println("i: " + i + " [" + base[i] + ", " + check[i]
+ "]");
}
}
}
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2.AC自动机方案
Aho-Corasick（AC）自动机是一种建立在 Trie 树上的一种改进算法，是一种多模式匹配算法
AC 自动机算法使用 Trie 树来存放模式串的前缀，通过失败匹配指针（失配指针）来处理匹配失败的跳转
如果使用上面提到的 DAT 来表示 AC 自动机 ，就可以兼顾两者的优点，得到一种高效的多模式匹配算法
https://github.com/hankcs/AhoCorasickDoubleArrayTrie
3.DFA算法方案
DFA即确定有穷自动机，与之对应的是 NFA（不确定有穷自动机)
hutool代码仓中提供了 DFA 算法的实现：
https://github.com/dromara/hutool/tree/v5-master/hutool-dfa/src/main/java/cn/hutool/dfa
使用案例：
WordTree wordTree = new WordTree();
wordTree.addWord("大");
wordTree.addWord("大憨憨");
wordTree.addWord("憨憨");
String text = "那人真是个大憨憨！";
// 获得第一个匹配的关键字
String matchStr = wordTree.match(text);
System.out.println(matchStr);
// 标准匹配，匹配到最短关键词，并跳过已经匹配的关键词
List matchStrList = wordTree.matchAll(text, -1, false, false);
System.out.println(matchStrList);
//匹配到最长关键词，跳过已经匹配的关键词
List matchStrList2 = wordTree.matchAll(text, -1, false, true);
System.out.println(matchStrList2);
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4.开源方案
1、https://github.com/toolgood/ToolGood.Words
一款高性能敏感词(非法词/脏字)检测过滤组件，附带繁体简体互换，支持全角半角互换，汉字转拼音，模糊搜索等功能
2、https://github.com/hooj0/sensitive-words-filter
敏感词过滤项目，提供 TTMP、DFA、DAT、hash bucket、Tire 算法支持过滤。可以支持文本的高亮、过滤、判词、替换的接口支持
3、敏感词数据（涉黄政黑）
https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words
文章知识点与官方知识档案匹配，可进一步学习相关知识
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版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
原文链接：https://blog.csdn.net/Gherbirthday0916/article/details/140277154
