Personal Data Leak Checker – Automated Breach Scanning & Alerts

1. Introduction

The Personal Data Leak Checker Using Web Scraping is a Python-based cybersecurity project that helps users detect whether their personal information (email IDs, phone numbers, usernames) has appeared in public breaches or leaked databases. It automates searches across paste sites, breach forums, and open APIs, and alerts users with findings and clear steps to mitigate risk. The tool improves personal cybersecurity awareness and protects digital identity.

2. Existing System vs Proposed System

3. Working

1. User Input: Enter email, phone, or username.
2. Scraping & APIs: Query public breach DBs, paste sites, and indexed forums.
3. Matching: Normalize and compare extracted data with input.
4. Result Analysis: Identify breach name, date, data types exposed.
5. Alert Generation: Display findings + email alert (optional).
6. Report Logging: Store encrypted history locally for audits.

4. Technology Stack

• Language: Python
• Libraries: requests, BeautifulSoup, re, json, pandas, smtplib, tkinter/Flask
• APIs: HaveIBeenPwned (or compatible) & custom sources
• Backend: SQLite3 for scan history + alerts
• Interface: CLI or Flask dashboard
• Security: AES encryption for stored inputs/results

5. Modules

6. Advantages

• Automated, multi-source leak detection.
• Early warning helps prevent account misuse.
• Beginner-friendly UI; no expertise needed.
• Supports email/phone/username scans.
• Extensible for enterprise monitoring.

7. Applications

• Personal cybersecurity & footprint monitoring.
• Employee credential safety checks.
• Educational labs on scraping & security.
• Ethical hacking/awareness programs.
• Integrations with password managers/audits.

Python Integration Sketch (Requests + BS4 + APIs)

import re, json, time, sqlite3, requests
from bs4 import BeautifulSoup
from Crypto.Cipher import AES  # optional; use Fernet/cryptography in practice

HEADERS = {"User-Agent":"T2T-LeakChecker/1.0"}
SOURCES = [
    {"name":"Public Paste Mirror","url":"https://example.com/search?q={q}","type":"html","selector":".result"},
    {"name":"HIBP API","url":"https://haveibeenpwned.com/api/v3/breachedaccount/{q}","type":"json","auth":"HIBP_KEY"}
]

def normalize(q):
    q = q.strip()
    return q.lower()

def search_source(src, q):
    url = src["url"].format(q=q)
    h = HEADERS.copy()
    if "auth" in src:
        h["hibp-api-key"] = ""
    r = requests.get(url, headers=h, timeout=15)
    r.raise_for_status()
    if src["type"] == "html":
        soup = BeautifulSoup(r.text, "html.parser")
        items = [el.get_text(strip=True) for el in soup.select(src["selector"])]
        return [{"source":src["name"],"raw":it} for it in items]
    else:
        data = r.json()
        return [{"source":src["name"],"raw":json.dumps(data)}]

def detect_matches(q, items):
    # naive match; in practice use structured parsing + fuzzy logic
    hits = []
    for it in items:
        if q in it["raw"].lower():
            hits.append(it["source"])
    return list(set(hits))

def recommend():
    return [
        "Reset passwords and enable 2FA.",
        "Check reuse across accounts; rotate quickly.",
        "Monitor inbox/SMS for suspicious resets."
    ]

def run_scan(query):
    q = normalize(query)
    all_items = []
    for src in SOURCES:
        try:
            all_items.extend(search_source(src, q))
            time.sleep(1.2)  # polite delay
        except Exception:
            continue
    sources = detect_matches(q, all_items)
    return {
        "query": q,
        "found": bool(sources),
        "sources": sources,
        "recommendations": recommend()
    }

if __name__ == "__main__":
    print(run_scan("demo@example.com"))
              

What You Get