Masking Personal Information - Solution & Explanation

Problem Overview: You receive a string that represents either an email address or a phone number. The task is to mask sensitive parts of the string while preserving a few characters so the result still identifies the contact. Email masking keeps the first and last character of the name while replacing the middle with asterisks. Phone masking hides all digits except the last four and formats the result depending on whether a country code exists.

Approach 1: Email and Phone Number Detection and Masking (O(n) time, O(n) space)

The input type determines the masking rule. Scan the string once to check for the '@' character. If it exists, treat the input as an email; otherwise treat it as a phone number. For emails, convert everything to lowercase, keep the first and last character of the local name, and insert five asterisks between them before appending the domain. For phone numbers, iterate through the string and extract only digits, ignoring characters like '(', ')', '+', spaces, or dashes. Keep the last four digits visible and replace the rest with *. If more than ten digits exist, prepend the correct country code mask using +***- style formatting. This approach relies purely on string manipulation and a single pass through the input.

The key insight is separating detection from transformation. Once you know the input type, the masking rule becomes deterministic. You perform simple operations such as substring extraction, digit filtering, and concatenation. Time complexity is O(n) because each character is processed at most once, and space complexity is O(n) for constructing the masked output.

Approach 2: Regular Expression for Detecting Number of Digits (O(n) time, O(n) space)

This variation uses regular expressions to simplify parsing. Instead of manually iterating to filter digits, apply a regex pattern like \d to extract all digits from the input. The resulting list directly reveals how many digits the phone number contains, which determines the country code length. For emails, a simple split around '@' combined with lowercase conversion produces the masked format quickly.

Regex makes the implementation shorter and easier to read in languages that support strong pattern libraries. Digit extraction and counting happen in one step, avoiding manual conditional checks. Complexity remains O(n) time because the regex engine scans the string once, and O(n) space for storing extracted characters.

Recommended for interviews: The direct detection and masking approach is what interviewers usually expect. It shows that you can handle conditional parsing, apply exact formatting rules, and reason through edge cases such as international phone numbers. Regex solutions are concise and elegant but sometimes hide the logic interviewers want to see. Demonstrating the explicit string processing steps proves you understand how the masking rules are implemented.