Masking Personal Information - Practice Coding Problems

Masking Personal Information - Problem

String Medium

Masking Personal Information is a common privacy protection technique used in data processing systems. You're given a personal information string s that represents either an email address or a phone number. Your task is to return the properly masked version following specific rules.

📧 Email Masking Rules:
• Convert all letters to lowercase
• Keep only the first and last letter of the name part (before @)
• Replace middle letters with exactly 5 asterisks "*****"
• Example: "John@Example.com" → "j*****n@example.com"

📱 Phone Number Masking Rules:
• Remove all separators: +, -, (, ), space
• Last 10 digits are the local number, remaining 0-3 digits are country code
• Format based on country code length:
  - 0 digits: "***-***-XXXX"
  - 1 digit: "+*-***-***-XXXX"
  - 2 digits: "+**-***-***-XXXX"
  - 3 digits: "+***-***-***-XXXX"
• XXXX represents the last 4 digits of the local number

Input & Output

example_1.py — Email Masking

$ Input: s = "LeetCode@LeetCode.com"

› Output: "l*****e@leetcode.com"

💡 Note: The email name 'LeetCode' becomes 'l*****e' (first + 5 asterisks + last), and everything is converted to lowercase

example_2.py — Domestic Phone

$ Input: s = "1(234)567-890"

› Output: "***-***-7890"

💡 Note: After removing separators we get '1234567890' (10 digits), so no country code. Format is '***-***-XXXX' where XXXX is '7890'

example_3.py — International Phone

$ Input: s = "86-(10)12345678"

› Output: "+**-***-***-5678"

💡 Note: After removing separators we get '861012345678' (12 digits), so 2-digit country code '86'. Format is '+**-***-***-XXXX'

Constraints

1 ≤ s.length ≤ 40
s is either a valid email or a phone number
If s is an email: name and domain contain only letters, domain has exactly one '.' separator
If s is a phone number: it contains 10-13 digits total

Visualization

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Understanding the Visualization

Document Scanning

Scanner detects '@' symbol to classify as email vs phone number

Email Protocol

Apply email masking: convert to lowercase, keep first/last of name, mask middle with 5 stars

Phone Protocol

Apply phone masking: extract digits, determine country code length, format accordingly

Output Generation

Return properly masked result following privacy standards

Key Takeaway

🎯 Key Insight: Pattern recognition allows instant classification - emails have '@' symbols, phones are pure digits after separator removal, enabling efficient single-pass processing with appropriate masking protocols.

Asked in

f Facebook 12 a Amazon 8 G Google 6 ⊞ Microsoft 4

The optimal approach uses pattern recognition to instantly determine if the input is an email (contains '@') or phone number (all digits after removing separators). For emails, we extract the name part and apply the masking rule: first letter + '*****' + last letter + '@' + domain (all lowercase). For phones, we count digits to determine country code length and apply the corresponding format with the last 4 digits visible.

Common Approaches

Approach	Time	Space	Notes
✓ Character-by-Character Analysis	O(n)	O(n)	Iterate through each character to determine type and apply masking rules

Character-by-Character Analysis — Algorithm Steps

Scan the entire string to check for '@' symbol to determine if it's email or phone
If email: find '@', extract name part, keep first and last char, replace middle with '*****'
If phone: remove all separators, count digits, determine country code length
Apply appropriate phone masking format based on country code length
Return the masked result

Visualization

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Step-by-Step Walkthrough

Type Detection

Scan for '@' symbol to determine if input is email or phone number

Email Processing

If email: extract name part, keep first/last chars, replace middle with '*****'

Phone Processing

If phone: remove separators, count digits, determine country code length

Apply Format

Apply appropriate masking format based on the detected type and structure

Code -

solution.c — C

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

char* maskPII(char* s) {
    if (strchr(s, '@') != NULL) {
        return maskEmail(s);
    } else {
        return maskPhone(s);
    }
}

char* maskEmail(char* email) {
    int len = strlen(email);
    char* result = (char*)malloc(len + 10);
    
    // Convert to lowercase
    for (int i = 0; i < len; i++) {
        email[i] = tolower(email[i]);
    }
    
    char* atPos = strchr(email, '@');
    int atIndex = atPos - email;
    
    sprintf(result, "%c*****%c%s", email[0], email[atIndex-1], atPos);
    return result;
}

char* maskPhone(char* phone) {
    char digits[20];
    int digitCount = 0;
    
    // Extract digits
    for (int i = 0; phone[i]; i++) {
        if (isdigit(phone[i])) {
            digits[digitCount++] = phone[i];
        }
    }
    
    char* result = (char*)malloc(30);
    int countryCodeLen = digitCount - 10;
    
    if (countryCodeLen == 0) {
        sprintf(result, "***-***-%.4s", digits + digitCount - 4);
    } else {
        sprintf(result, "+%.*s-***-***-%.4s", 
                countryCodeLen, "***", digits + digitCount - 4);
    }
    
    return result;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

We scan through the string multiple times - once to determine type, once to process

✓ Linear Growth

Space Complexity

O(n)

We create new strings for the masked result and intermediate processing

⚡ Linearithmic Space

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Smart Actions

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Character-by-Character Analysis — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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