LeetCode #2227 — HARD

Encrypt and Decrypt Strings

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given a character array keys containing unique characters and a string array values containing strings of length 2. You are also given another string array dictionary that contains all permitted original strings after decryption. You should implement a data structure that can encrypt or decrypt a 0-indexed string.

A string is encrypted with the following process:

  1. For each character c in the string, we find the index i satisfying keys[i] == c in keys.
  2. Replace c with values[i] in the string.

Note that in case a character of the string is not present in keys, the encryption process cannot be carried out, and an empty string "" is returned.

A string is decrypted with the following process:

  1. For each substring s of length 2 occurring at an even index in the string, we find an i such that values[i] == s. If there are multiple valid i, we choose any one of them. This means a string could have multiple possible strings it can decrypt to.
  2. Replace s with keys[i] in the string.

Implement the Encrypter class:

  • Encrypter(char[] keys, String[] values, String[] dictionary) Initializes the Encrypter class with keys, values, and dictionary.
  • String encrypt(String word1) Encrypts word1 with the encryption process described above and returns the encrypted string.
  • int decrypt(String word2) Returns the number of possible strings word2 could decrypt to that also appear in dictionary.

Example 1:

Input
["Encrypter", "encrypt", "decrypt"]
[[['a', 'b', 'c', 'd'], ["ei", "zf", "ei", "am"], ["abcd", "acbd", "adbc", "badc", "dacb", "cadb", "cbda", "abad"]], ["abcd"], ["eizfeiam"]]
Output
[null, "eizfeiam", 2]

Explanation
Encrypter encrypter = new Encrypter([['a', 'b', 'c', 'd'], ["ei", "zf", "ei", "am"], ["abcd", "acbd", "adbc", "badc", "dacb", "cadb", "cbda", "abad"]);
encrypter.encrypt("abcd"); // return "eizfeiam". 
                           // 'a' maps to "ei", 'b' maps to "zf", 'c' maps to "ei", and 'd' maps to "am".
encrypter.decrypt("eizfeiam"); // return 2. 
                              // "ei" can map to 'a' or 'c', "zf" maps to 'b', and "am" maps to 'd'. 
                              // Thus, the possible strings after decryption are "abad", "cbad", "abcd", and "cbcd". 
                              // 2 of those strings, "abad" and "abcd", appear in dictionary, so the answer is 2.

Constraints:

  • 1 <= keys.length == values.length <= 26
  • values[i].length == 2
  • 1 <= dictionary.length <= 100
  • 1 <= dictionary[i].length <= 100
  • All keys[i] and dictionary[i] are unique.
  • 1 <= word1.length <= 2000
  • 2 <= word2.length <= 200
  • All word1[i] appear in keys.
  • word2.length is even.
  • keys, values[i], dictionary[i], word1, and word2 only contain lowercase English letters.
  • At most 200 calls will be made to encrypt and decrypt in total.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given a character array keys containing unique characters and a string array values containing strings of length 2. You are also given another string array dictionary that contains all permitted original strings after decryption. You should implement a data structure that can encrypt or decrypt a 0-indexed string. A string is encrypted with the following process: For each character c in the string, we find the index i satisfying keys[i] == c in keys. Replace c with values[i] in the string. Note that in case a character of the string is not present in keys, the encryption process cannot be carried out, and an empty string "" is returned. A string is decrypted with the following process: For each substring s of length 2 occurring at an even index in the string, we find an i such that values[i] == s. If there are multiple valid i, we choose any one of them. This means a string

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map · Design · Trie

Example 1

["Encrypter","encrypt","decrypt"]
[[["a","b","c","d"],["ei","zf","ei","am"],["abcd","acbd","adbc","badc","dacb","cadb","cbda","abad"]],["abcd"],["eizfeiam"]]

Related Problems

  • Implement Trie (Prefix Tree) (implement-trie-prefix-tree)
  • Word Search II (word-search-ii)
  • Implement Trie II (Prefix Tree) (implement-trie-ii-prefix-tree)
  • Find the Sum of Encrypted Integers (find-the-sum-of-encrypted-integers)
Step 02

Core Insight

What unlocks the optimal approach

  • For encryption, use hashmap to map each char of word1 to its value.
  • For decryption, use trie to prune when necessary.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2227: Encrypt and Decrypt Strings
class Encrypter {
    private Map<Character, String> mp = new HashMap<>();
    private Map<String, Integer> cnt = new HashMap<>();

    public Encrypter(char[] keys, String[] values, String[] dictionary) {
        for (int i = 0; i < keys.length; ++i) {
            mp.put(keys[i], values[i]);
        }
        for (String w : dictionary) {
            cnt.merge(encrypt(w), 1, Integer::sum);
        }
    }

    public String encrypt(String word1) {
        StringBuilder sb = new StringBuilder();
        for (char c : word1.toCharArray()) {
            if (!mp.containsKey(c)) {
                return "";
            }
            sb.append(mp.get(c));
        }
        return sb.toString();
    }

    public int decrypt(String word2) {
        return cnt.getOrDefault(word2, 0);
    }
}

/**
 * Your Encrypter object will be instantiated and called as such:
 * Encrypter obj = new Encrypter(keys, values, dictionary);
 * String param_1 = obj.encrypt(word1);
 * int param_2 = obj.decrypt(word2);
 */
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(1) per op
Space
O(n)

Approach Breakdown

NAIVE
O(n) per op time
O(n) space

Use a simple list or array for storage. Each operation (get, put, remove) requires a linear scan to find the target element — O(n) per operation. Space is O(n) to store the data. The linear search makes this impractical for frequent operations.

OPTIMIZED DESIGN
O(1) per op time
O(n) space

Design problems target O(1) amortized per operation by combining data structures (hash map + doubly-linked list for LRU, stack + min-tracking for MinStack). Space is always at least O(n) to store the data. The challenge is achieving constant-time operations through clever structure composition.

Shortcut: Combine two data structures to get O(1) for each operation type. Space is always O(n).
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.