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Privacy & Security
Dragon edited this page Dec 22, 2025
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1 revision
Comprehensive guide to maximizing your privacy and security while using NaviDuck. Learn about built-in protections, configuration options, and best practices.
Last updated: 12/22/2025
- 🔐 Privacy Fundamentals
- 🛡️ Built-in Security Features
- 🧅 Tor & Anonymous Browsing
- 🚫 Data Collection & Tracking
- 🔧 Privacy Configuration
- 🕵️ Threat Mitigation
- 📊 Privacy Benchmarks
- ⚖️ Legal Considerations
- 🚨 Emergency Protocols
- ✅ Search queries (local storage only)
- ✅ Browsing history (local storage only)
- ✅ Bookmarks (local storage only)
- ✅ AI conversations (not logged externally)
- ✅ IP address (when using Tor)
- ❌ DNS requests (reveal sites you visit)
- ❌ HTTP headers (browser fingerprint)
- ❌ TLS handshake (reveals IP to sites)
- ❌ WebRTC leaks (not applicable in CLI)
- ❌ Third-party tracking (on visited websites)
Target: Casual snoopers, network admins, ISP
Protection: Local encryption, no cloud sync
Risk: Medium
Target: Advertisers, data brokers, basic tracking
Protection: Tor, DNS encryption, request filtering
Risk: Low
Target: State actors, advanced trackers
Protection: Tor bridges, VPN chain, request obfuscation
Risk: Very Low
# All data stored locally in user directory
~/.naviduck_data.json # History, bookmarks
~/.naviduck_config.json # Settings
~/.naviduck_cache/ # Search cache (optional)
# No cloud sync or external serversOption A: Built-in Simple Encryption
# Enable in config:
{
"encryption": {
"enabled": true,
"method": "simple",
"password": "your-secure-passphrase"
}
}Option B: OS-Level Encryption
# Linux: Encrypt home directory
ecryptfs-setup-private
# Mac: FileVault
sudo fdesetup enable
# Windows: BitLocker
manage-bde -on C:Option C: Manual Encryption
# Add to BrowserState.save_data():
from cryptography.fernet import Fernet
import base64
def encrypt_data(self, data):
key = base64.urlsafe_b64encode(self.encryption_key)
cipher = Fernet(key)
encrypted = cipher.encrypt(json.dumps(data).encode())
return encrypted
def decrypt_data(self, encrypted):
key = base64.urlsafe_b64encode(self.encryption_key)
cipher = Fernet(key)
decrypted = cipher.decrypt(encrypted)
return json.loads(decrypted)# Default request headers (minimal fingerprint):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
# No referrer
# No cookies (by default)
# No tracking headers
}
# Randomize User-Agent (enhanced privacy):
user_agents = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36',
'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/115.0'
]
headers['User-Agent'] = random.choice(user_agents)# No persistent cookies by default
session = requests.Session()
session.cookies.clear() # Clear cookies on start
# Optionally enable cookie jar with encryption:
import pickle
from cryptography.fernet import Fernet
class EncryptedCookieJar:
def __init__(self, key):
self.cipher = Fernet(key)
self.cookies = {}
def save(self, filename):
encrypted = self.cipher.encrypt(pickle.dumps(self.cookies))
with open(filename, 'wb') as f:
f.write(encrypted)
def load(self, filename):
with open(filename, 'rb') as f:
encrypted = f.read()
self.cookies = pickle.loads(self.cipher.decrypt(encrypted))SEARCH_ENGINES_PRIVACY = {
"ddg": {
"name": "DuckDuckGo",
"privacy_rating": 9, # 1-10
"logs": "No personal logs",
"tracking": "No third-party tracking",
"location": "No precise location",
"recommended": True
},
"brave": {
"name": "Brave Search",
"privacy_rating": 8,
"logs": "Anonymous logs",
"tracking": "No tracking",
"location": "Country-level only",
"recommended": True
},
"ddg_api": {
"name": "DuckDuckGo API",
"privacy_rating": 9,
"logs": "No personal logs",
"tracking": "No tracking",
"location": "None",
"recommended": True
},
"wikipedia": {
"name": "Wikipedia",
"privacy_rating": 10,
"logs": "Public logs (no IP storage)",
"tracking": "No tracking",
"location": "None",
"recommended": True
},
"google": {
"name": "Google",
"privacy_rating": 2,
"logs": "Extensive logging",
"tracking": "Comprehensive tracking",
"location": "Precise location",
"recommended": False
}
}# Set in ~/.naviduck_config.json
{
"privacy_mode": true,
"default_engine": "ddg", # DuckDuckGo is most private
"engines": {
"ddg": true,
"ddg_api": true,
"brave": true,
"wikipedia": true,
"google": false # Disable low-privacy engines
},
"clear_history_on_exit": false,
"encrypt_local_data": true,
"disable_cookies": true,
"randomize_user_agent": true
}# Enhanced SSL configuration
import ssl
import urllib3
# Disable weak protocols
context = ssl.create_default_context()
context.minimum_version = ssl.TLSVersion.TLSv1_2
context.set_ciphers('ECDHE+AESGCM:ECDHE+CHACHA20:DHE+AESGCM:DHE+CHACHA20')
# Certificate pinning (optional)
allowed_certificates = {
'duckduckgo.com': 'sha256/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=',
'wikipedia.org': 'sha256/BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB='
}
def verify_certificate(cert, host):
fingerprint = hashlib.sha256(cert).hexdigest()
expected = allowed_certificates.get(host)
if expected and fingerprint != expected:
raise ssl.SSLCertVerificationError("Certificate pinning failure")# Encrypted DNS configuration
ENCRYPTED_DNS_SERVERS = {
"cloudflare": {
"doh": "https://cloudflare-dns.com/dns-query",
"dot": "1.1.1.1",
"privacy": "No logging"
},
"quad9": {
"doh": "https://dns.quad9.net/dns-query",
"dot": "9.9.9.9",
"privacy": "No logging, blocks malware"
},
"nextdns": {
"doh": "https://dns.nextdns.io/",
"dot": "45.90.28.0",
"privacy": "Custom filtering, logging optional"
}
}
# Implementation using dnspython
import dns.resolver
import dns.https
def setup_encrypted_dns(provider="cloudflare"):
resolver = dns.resolver.Resolver(configure=False)
resolver.nameservers = [ENCRYPTED_DNS_SERVERS[provider]["dot"]]
# Or use DNS-over-HTTPS
session = dns.https.HTTPSession()
return resolverBasic Tor (Default):
# Uses system Tor or Tor Browser
tor start # Starts Tor on port 9050Enhanced Tor Configuration:
# In TorManager, add advanced options:
advanced_config = {
"use_bridges": True,
"bridge_type": "obfs4",
"entry_nodes": "{us}",
"exit_nodes": "{se},{nl},{ch}",
"exclude_nodes": "{cn},{ru},{sy}",
"strict_nodes": True,
"max_streams": 100,
"circuit_timeout": 60
}Double Tor Proxy Chain:
def create_tor_chain():
"""Create multi-hop Tor circuit"""
chain_proxies = {
'http': 'socks5h://127.0.0.1:9050',
'https': 'socks5h://127.0.0.1:9050'
}
# First hop through local Tor
response = session.get(url, proxies=chain_proxies)
# Second hop through external Tor proxy (if available)
external_tor = 'socks5h://tor-exit-node.onion:9050'
# Note: Requires authentication and trusted exit nodeTor + VPN Chain:
def tor_over_vpn():
"""Route Tor through VPN for extra layer"""
# 1. Connect to VPN first
# 2. Then connect to Tor
proxies = {
'http': 'socks5h://127.0.0.1:9050',
}
# VPN provides first layer of IP masking
# Tor provides anonymity layerBridge Configuration:
bridges = [
"obfs4 192.95.36.142:443 CDF2E852BF539B82BD10E27E9115A31734E378C2 cert=qUVQ0srL1JI/vO6V6m/24anYXiJD3QP2HgzUKQtQ7GRqqUvs7P+tG43RtAqdhLOALP7DJQ iat-mode=0",
"obfs4 37.218.245.14:38224 D9A82D2F9C2F65A18407B1D2B764F130847F8B5D cert=bjRaMrr1BRiAW8IE9U5z27fQaYgOhX1UCmOpg2pFpoMvo6ZgQMzLsaTzzQNTlm7hNcb+Sg iat-mode=0",
]
def configure_bridges():
torrc_content = """
UseBridges 1
ClientTransportPlugin obfs4 exec /usr/bin/obfs4proxy
"""
for bridge in bridges:
torrc_content += f"Bridge {bridge}\n"
with open("torrc_custom", "w") as f:
f.write(torrc_content)
# Start Tor with custom config
subprocess.run(["tor", "-f", "torrc_custom"])Access .onion Sites:
# Direct .onion access (requires Tor)
tor start
go http://duckduckgogg42xjoc72x3sjasowoarfbgcmvfimaftt6twagswzczad.onionOnion Service Discovery:
ONION_SEARCH_ENGINES = {
"onion_ddg": {
"name": "DuckDuckGo Onion",
"url": "http://duckduckgogg42xjoc72x3sjasowoarfbgcmvfimaftt6twagswzczad.onion/html/",
"requires_tor": True,
"privacy": "Maximum"
},
"ahmia": {
"name": "Ahmia Onion Search",
"url": "http://juhanurmihxlp77nkq76byazcldy2hlmovfu2epvl5ankdibsot4csyd.onion/",
"requires_tor": True,
"privacy": "Maximum"
}
}Max Privacy (Slowest):
tor_config = {
"max_circuit_dirtiness": 600, # 10 minutes
"new_circuit_period": 600, # New circuit every 10 min
"num_entry_guards": 5, # More guards = more stable
"use_microdescriptors": 0, # Full descriptors
"safe_socks": 1, # Reject unsafe ports
"test_socks": 1, # Test socks proxy
}Balanced (Recommended):
tor_config = {
"max_circuit_dirtiness": 300, # 5 minutes
"new_circuit_period": 300,
"num_entry_guards": 3,
"use_microdescriptors": 1, # Faster
"safe_socks": 1,
}Max Speed (Less Private):
tor_config = {
"max_circuit_dirtiness": 60, # 1 minute
"new_circuit_period": 60,
"num_entry_guards": 1,
"use_microdescriptors": 1,
"safe_socks": 0, # Allow all ports
}def get_tor_circuit_info():
"""Get information about current Tor circuit"""
import stem.control
with stem.control.Controller.from_port() as controller:
controller.authenticate()
circuits = controller.get_circuits()
for circuit in circuits:
if circuit.status == 'BUILT':
print(f"Circuit ID: {circuit.id}")
print(f"Path: {' -> '.join(circuit.path)}")
print(f"Purpose: {circuit.purpose}")def new_tor_circuit():
"""Force new Tor circuit"""
with stem.control.Controller.from_port() as controller:
controller.authenticate()
controller.signal('NEWNYM') # New identity
print("New Tor circuit created")class AutoRotatingTor:
def __init__(self, rotation_interval=300):
self.rotation_interval = rotation_interval
self.last_rotation = time.time()
self.request_count = 0
def should_rotate(self):
"""Determine if circuit should be rotated"""
time_elapsed = time.time() - self.last_rotation
should_rotate = (
time_elapsed > self.rotation_interval or
self.request_count > 100
)
if should_rotate:
self.last_rotation = time.time()
self.request_count = 0
return True
return FalseRandomized HTTP Headers:
def get_randomized_headers():
"""Generate unique but realistic headers"""
user_agents = [...] # List of 100+ user agents
accept_languages = [
'en-US,en;q=0.9',
'en-GB,en;q=0.8',
'en-CA,en;q=0.7',
'en-AU,en;q=0.6'
]
accept_encodings = [
'gzip, deflate, br',
'gzip, deflate',
'br, gzip, deflate'
]
return {
'User-Agent': random.choice(user_agents),
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Language': random.choice(accept_languages),
'Accept-Encoding': random.choice(accept_encodings),
'DNT': '1', # Do Not Track
'Upgrade-Insecure-Requests': '1',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'none',
'Sec-Fetch-User': '?1',
'Cache-Control': 'max-age=0'
}Canvas Fingerprint Protection:
# Not applicable to CLI browser, but for awareness:
# CLI browsers don't have:
# - Canvas API
# - WebGL
# - AudioContext
# - Font enumeration
# This is a privacy advantage over graphical browsersURL Cleaning:
def clean_tracking_params(url):
"""Remove tracking parameters from URLs"""
from urllib.parse import urlparse, parse_qs, urlunparse
parsed = urlparse(url)
query_params = parse_qs(parsed.query)
# Common tracking parameters
tracking_params = {
'utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'msclkid', 'dclid', 'mc_eid',
'_ga', '_gl', 'gclsrc', 'vero_conv', 'vero_id',
'hmb_campaign', 'hmb_medium', 'hmb_source',
'ref', 'source', 'referrer', 'referral',
'clickId', 'affiliate', 'aff_id', 'campaign',
'igshid', 'fb_action_ids', 'fb_action_types'
}
# Remove tracking parameters
cleaned_params = {}
for key, values in query_params.items():
if key.lower() not in tracking_params:
cleaned_params[key] = values[0] if values else ''
# Reconstruct URL
cleaned_query = '&'.join(f"{k}={v}" for k, v in cleaned_params.items())
cleaned_url = urlunparse((
parsed.scheme,
parsed.netloc,
parsed.path,
parsed.params,
cleaned_query,
parsed.fragment
))
return cleaned_urlReferrer Policy:
# Never send referrer
session.headers.update({
'Referrer-Policy': 'no-referrer',
# Or for specific cases:
# 'Referrer-Policy': 'same-origin'
})
# Remove referrer from individual requests
def make_request_no_referrer(url):
headers = session.headers.copy()
headers.pop('Referer', None)
return session.get(url, headers=headers)Strict Cookie Policy:
class PrivacyCookieJar:
def __init__(self):
self.allowed_domains = set()
self.blocked_domains = {
'doubleclick.net',
'google-analytics.com',
'facebook.com',
'twitter.com',
'googlesyndication.com',
'scorecardresearch.com',
'outbrain.com',
'taboola.com'
}
def should_accept_cookie(self, domain, name, value):
# Block known trackers
if any(tracker in domain for tracker in self.blocked_domains):
return False
# Session cookies only (no persistence)
if 'expires' in value.lower() or 'max-age' in value.lower():
return False
return TrueFirst-Party Isolation:
# Implement cookie jars per domain
domain_cookie_jars = {}
def get_cookie_jar_for_domain(domain):
if domain not in domain_cookie_jars:
domain_cookie_jars[domain] = requests.cookies.RequestsCookieJar()
return domain_cookie_jars[domain]
# Use separate cookie jar for each domain
def request_with_isolation(url):
domain = urlparse(url).netloc
jar = get_cookie_jar_for_domain(domain)
# Make request with isolated cookies
return session.get(url, cookies=jar)DNS-Level Blocking:
# Use blocklists in DNS resolution
BLOCKLISTS = {
'adaway': 'https://raw.githubusercontent.com/AdAway/adaway.github.io/master/hosts.txt',
'stevenblack': 'https://raw.githubusercontent.com/StevenBlack/hosts/master/hosts',
'someonewhocares': 'https://someonewhocares.org/hosts/zero/hosts'
}
def setup_dns_blocking():
"""Load blocklists and filter DNS requests"""
blocked_domains = set()
for name, url in BLOCKLISTS.items():
try:
response = requests.get(url)
# Parse hosts file format
for line in response.text.split('\n'):
if line.strip() and not line.startswith('#'):
parts = line.split()
if len(parts) >= 2:
blocked_domains.add(parts[1])
except:
pass
return blocked_domains
class BlockingResolver:
def __init__(self):
self.blocked = setup_dns_blocking()
self.resolver = dns.resolver.Resolver()
def resolve(self, domain):
if domain in self.blocked:
# Return loopback address for blocked domains
return ['127.0.0.1']
return self.resolver.resolve(domain)Request Filtering:
# Filter outgoing requests
def filter_request(url, headers):
"""Filter requests based on privacy rules"""
# Block known trackers
tracker_patterns = [
r'google-analytics\.com',
r'googlesyndication\.com',
r'doubleclick\.net',
r'facebook\.com/tr',
r'analytics\.twitter\.com',
]
for pattern in tracker_patterns:
if re.search(pattern, url):
return False, "Blocked tracker"
# Block certain content types
if headers.get('Accept', ''):
if 'image/webp' in headers['Accept']:
# Optionally block images for faster loading
pass
return True, "Allowed"{
"privacy_level": "basic",
"features": {
"clear_history_on_exit": false,
"encrypt_local_data": false,
"randomize_user_agent": false,
"strip_tracking_params": true,
"block_known_trackers": false,
"dns_over_https": false,
"tor_enabled": false
},
"search_engines": ["ddg", "wikipedia"],
"data_retention": {
"history_days": 90,
"cache_days": 7
}
}{
"privacy_level": "enhanced",
"features": {
"clear_history_on_exit": true,
"encrypt_local_data": true,
"randomize_user_agent": true,
"strip_tracking_params": true,
"block_known_trackers": true,
"dns_over_https": true,
"tor_enabled": false,
"first_party_isolation": true
},
"search_engines": ["ddg_api", "brave"],
"data_retention": {
"history_days": 30,
"cache_days": 1
}
}{
"privacy_level": "maximum",
"features": {
"clear_history_on_exit": true,
"encrypt_local_data": true,
"randomize_user_agent": true,
"strip_tracking_params": true,
"block_known_trackers": true,
"dns_over_https": true,
"tor_enabled": true,
"first_party_isolation": true,
"cookie_isolation": true,
"circuit_rotation": 60,
"use_bridges": true,
"onion_only": false
},
"search_engines": ["ddg_api"],
"data_retention": {
"history_days": 0, # Don't store history
"cache_days": 0 # Don't cache
}
}def privacy_configuration_wizard():
"""Interactive privacy setup"""
print("🔒 Privacy Configuration Wizard")
print("=" * 40)
profiles = {
"1": ("Basic", "Minimal protection, maximum convenience"),
"2": ("Enhanced", "Good protection, balanced performance"),
"3": ("Maximum", "Maximum protection, slower performance")
}
for key, (name, desc) in profiles.items():
print(f"{key}. {name}: {desc}")
choice = input("\nSelect privacy level (1-3): ")
if choice == "1":
config = BASIC_PRIVACY
elif choice == "2":
config = ENHANCED_PRIVACY
elif choice == "3":
config = MAXIMUM_PRIVACY
print("\n⚠️ Maximum privacy enables Tor and may be slow")
confirm = input("Continue? (y/N): ")
if confirm.lower() != 'y':
return privacy_configuration_wizard()
else:
print("Invalid choice")
return
# Apply configuration
apply_privacy_config(config)
print(f"\n✅ {profiles[choice][0]} privacy profile applied")def privacy_audit():
"""Check current privacy settings"""
print("🔍 Privacy Audit Report")
print("=" * 40)
checks = [
("Local data encrypted", check_encryption()),
("Tor enabled", check_tor()),
("DNS encrypted", check_dns()),
("Tracking params stripped", check_param_stripping()),
("User-agent randomized", check_ua_randomization()),
("History retention", check_history_retention()),
("Cache encryption", check_cache_encryption()),
]
score = 0
for check_name, status in checks:
icon = "✅" if status else "❌"
print(f"{icon} {check_name}")
if status:
score += 1
# Calculate privacy score
privacy_score = (score / len(checks)) * 100
print(f"\n📊 Privacy Score: {privacy_score:.0f}%")
if privacy_score < 50:
print("⚠️ Low privacy score - consider enabling more protections")
elif privacy_score < 80:
print("👍 Good privacy score")
else:
print("🎉 Excellent privacy score!")
return privacy_scoreimport schedule
import time
def schedule_privacy_tasks():
"""Schedule automatic privacy maintenance"""
# Clear cache daily
schedule.every().day.at("03:00").do(clear_old_cache)
# Rotate Tor circuits hourly
schedule.every().hour.do(rotate_tor_circuit)
# Clear history weekly (if configured)
schedule.every().sunday.at("04:00").do(clear_old_history)
# Update blocklists weekly
schedule.every().monday.at("02:00").do(update_blocklists)
print("🕐 Privacy tasks scheduled")
# Run scheduler in background
import threading
def run_scheduler():
while True:
schedule.run_pending()
time.sleep(60)
thread = threading.Thread(target=run_scheduler, daemon=True)
thread.start()# Pin certificates for critical services
CERTIFICATE_PINS = {
'duckduckgo.com': [
'sha256/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=',
'sha256/BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB='
],
'api.duckduckgo.com': [
'sha256/CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC='
]
}
class PinningAdapter(requests.adapters.HTTPAdapter):
def cert_verify(self, conn, url, verify, cert):
super().cert_verify(conn, url, verify, cert)
hostname = urlparse(url).hostname
if hostname in CERTIFICATE_PINS:
cert = conn.sock.getpeercert(binary_form=True)
cert_hash = hashlib.sha256(cert).digest()
b64_hash = base64.b64encode(cert_hash).decode()
if b64_hash not in CERTIFICATE_PINS[hostname]:
raise requests.exceptions.SSLError(
f"Certificate pinning violation for {hostname}"
)# Note: HPKP is deprecated but concept useful
class HPKPChecker:
def __init__(self):
self.hpkp_entries = {}
def check_hpkp(self, url, response):
hpkp_header = response.headers.get('Public-Key-Pins')
if hpkp_header:
# Parse and store pins
self.hpkp_entries[url] = parse_hpkp_header(hpkp_header)def resolve_doh(domain, provider="cloudflare"):
"""Resolve domain using DNS-over-HTTPS"""
doh_endpoints = {
"cloudflare": "https://cloudflare-dns.com/dns-query",
"google": "https://dns.google/dns-query",
"quad9": "https://dns.quad9.net/dns-query"
}
headers = {
'Accept': 'application/dns-json',
}
params = {
'name': domain,
'type': 'A',
'cd': 'false', # Checking disabled
}
response = requests.get(
doh_endpoints[provider],
headers=headers,
params=params
)
data = response.json()
return [answer['data'] for answer in data.get('Answer', [])]# Use dnspython for DNSSEC
import dns.resolver
import dns.dnssec
def validate_dnssec(domain):
"""Validate DNSSEC for a domain"""
resolver = dns.resolver.Resolver()
resolver.use_edns(0, dns.flags.DO, 4096)
try:
answer = resolver.resolve(domain, 'A')
# Check if DNSSEC was used
if answer.response.flags & dns.flags.AD:
return True, "DNSSEC validated"
else:
return False, "DNSSEC not available"
except dns.dnssec.ValidationFailure:
return False, "DNSSEC validation failed"def obfuscate_timing():
"""Add random delays to obscure timing patterns"""
# Add random delay before request
pre_delay = random.uniform(0.1, 2.0)
time.sleep(pre_delay)
# Make request
# Add random delay after request
post_delay = random.uniform(0.1, 1.0)
time.sleep(post_delay)def minimize_metadata(url):
"""Strip unnecessary metadata from requests"""
# Use generic path for common services
path_mappings = {
r'/search\?.*': '/search?q=',
r'/results\?.*': '/results?q=',
r'/wiki/.*': '/wiki/',
}
for pattern, replacement in path_mappings.items():
if re.match(pattern, url):
# Extract just the base path
parsed = urlparse(url)
new_path = replacement
return urlunparse((
parsed.scheme,
parsed.netloc,
new_path,
parsed.params,
'', # No query
'' # No fragment
))
return url# Implement per-origin storage
origin_storage = {}
def get_origin_storage(origin):
"""Get isolated storage for an origin"""
if origin not in origin_storage:
origin_storage[origin] = {
'cookies': {},
'cache': {},
'preferences': {}
}
return origin_storage[origin]
def make_isolated_request(url):
"""Make request with origin isolation"""
origin = urlparse(url).netloc
storage = get_origin_storage(origin)
# Use isolated cookies
cookies = storage['cookies']
response = session.get(url, cookies=cookies)
# Update isolated cookies
storage['cookies'].update(session.cookies.get_dict())
return response# Protect against various supercookie techniques
def check_supercookie_vulnerabilities():
"""Check for supercookie vulnerabilities"""
vulnerabilities = []
# Check ETag tracking
if has_etag_tracking():
vulnerabilities.append("ETag tracking possible")
# Check HSTS tracking
if has_hsts_tracking():
vulnerabilities.append("HSTS supercookie possible")
# Check favicon cache tracking
if has_favicon_tracking():
vulnerabilities.append("Favicon cache tracking")
return vulnerabilitiesdef check_url_safety(url):
"""Check if URL is safe using multiple services"""
safety_checks = []
# Google Safe Browsing (requires API key)
if GOOGLE_SAFE_BROWSING_API_KEY:
safe = check_google_safe_browsing(url)
safety_checks.append(("Google Safe Browsing", safe))
# PhishTank
phishtank_result = check_phishtank(url)
safety_checks.append(("PhishTank", phishtank_result))
# VirusTotal (requires API key)
if VIRUSTOTAL_API_KEY:
vt_result = check_virustotal(url)
safety_checks.append(("VirusTotal", vt_result))
# Local heuristics
heuristic_result = check_url_heuristics(url)
safety_checks.append(("Heuristics", heuristic_result))
# Aggregate results
safe_count = sum(1 for _, is_safe in safety_checks if is_safe)
total_checks = len(safety_checks)
if safe_count == total_checks:
return True, "All safety checks passed"
elif safe_count >= total_checks * 0.7:
return True, f"{safe_count}/{total_checks} checks passed"
else:
return False, f"Only {safe_count}/{total_checks} checks passed"def sanitize_content(content, content_type):
"""Sanitize potentially dangerous content"""
if 'text/html' in content_type:
# Remove scripts and dangerous elements
sanitized = re.sub(r'<script[^>]*>.*?</script>', '', content, flags=re.DOTALL)
sanitized = re.sub(r'on\w+="[^"]*"', '', sanitized)
sanitized = re.sub(r'javascript:', '', sanitized)
return sanitized
elif 'application/json' in content_type:
# Validate JSON structure
try:
data = json.loads(content)
# Remove any executable content
if isinstance(data, dict):
for key in list(data.keys()):
if 'script' in key.lower() or 'exec' in key.lower():
del data[key]
return json.dumps(data)
except:
return '{}'
return contentclass PrivacyBenchmark:
def __init__(self):
self.tests = [
("IP Address Leak", self.test_ip_leak),
("DNS Leak", self.test_dns_leak),
("WebRTC Leak", self.test_webrtc_leak),
("Browser Fingerprint", self.test_fingerprint),
("Tracking Protection", self.test_tracking),
("Cookie Isolation", self.test_cookie_isolation),
("Local Storage", self.test_local_storage),
("History Protection", self.test_history),
]
def run_benchmarks(self):
results = {}
for test_name, test_func in self.tests:
print(f"🔍 Testing: {test_name}")
try:
result = test_func()
results[test_name] = result
icon = "✅" if result["passed"] else "❌"
print(f" {icon} {result['message']}")
except Exception as e:
print(f" ⚠️ Test failed: {e}")
return self.calculate_score(results)
def test_ip_leak(self):
"""Test if real IP address is leaked"""
# Make request to IP checking service through proxy
test_urls = [
"https://api.ipify.org",
"https://checkip.amazonaws.com",
"https://icanhazip.com"
]
leaked_ips = []
for url in test_urls:
response = requests.get(url)
ip = response.text.strip()
leaked_ips.append(ip)
# Check if all IPs are the same (expected) and not real IP
unique_ips = set(leaked_ips)
if len(unique_ips) == 1:
return {"passed": True, "message": f"IP consistent: {list(unique_ips)[0]}"}
else:
return {"passed": False, "message": f"IP leak detected: {unique_ips}"}| Feature | NaviDuck (Max) | Chrome | Firefox | Tor Browser |
|---|---|---|---|---|
| IP Address Protection | Tor + VPN | None | Proxy only | Tor only |
| Fingerprinting | Minimal | Extensive | Some | Maximum |
| Tracking Protection | Built-in | Limited | Good | Maximum |
| Local Data Encryption | Yes | No | No | Partial |
| DNS Encryption | Optional | No | Optional | Yes |
| Cookie Isolation | Yes | No | Yes | Yes |
| History Protection | Encrypted | Plaintext | Plaintext | Encrypted |
| Open Source | Yes | No | Yes | Yes |
def privacy_dashboard():
"""Display current privacy status"""
import matplotlib.pyplot as plt
import numpy as np
metrics = {
'IP Protection': 85,
'Tracking Blocking': 90,
'Fingerprint Resistance': 70,
'Data Encryption': 95,
'DNS Security': 80,
'Cookie Control': 85
}
# Create radar chart
categories = list(metrics.keys())
values = list(metrics.values())
angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False).tolist()
values += values[:1]
angles += angles[:1]
fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(projection='polar'))
ax.plot(angles, values, 'o-', linewidth=2)
ax.fill(angles, values, alpha=0.25)
ax.set_xticks(angles[:-1])
ax.set_xticklabels(categories)
ax.set_ylim(0, 100)
ax.set_title('Privacy Protection Score', size=20, y=1.1)
plt.savefig('privacy_dashboard.png')
print("📊 Privacy dashboard saved as privacy_dashboard.png")
# Print summary
avg_score = sum(values[:-1]) / len(values[:-1])
print(f"\n📈 Average Privacy Score: {avg_score:.0f}%")
if avg_score >= 80:
print("🎉 Excellent privacy protection!")
elif avg_score >= 60:
print("👍 Good privacy protection")
else:
print("⚠️ Privacy protection needs improvement")- ✅ Data Minimization: NaviDuck collects minimal data
- ✅ Right to Access: Users can access their data
- ✅ Right to Erasure: Clear history/bookmarks feature
- ✅ Data Portability: Export data via JSON
- ✅ Privacy by Design: Built-in privacy features
- ✅ Do Not Sell: NaviDuck doesn't sell data
- ✅ Access Rights: Users can access collected data
- ✅ Deletion Rights: Users can delete their data
- ✅ Opt-Out: No tracking to opt-out from
- ✅ Similar compliance: Built-in privacy aligns with most regulations
class DataRetentionManager:
def __init__(self):
self.policies = {
'history': {
'default': 90, # days
'gdpr': 30,
'maximum_privacy': 0,
'legal_hold': 365
},
'cache': {
'default': 7,
'gdpr': 1,
'maximum_privacy': 0,
'legal_hold': 30
},
'logs': {
'default': 30,
'gdpr': 7,
'maximum_privacy': 0,
'legal_hold': 90
}
}
def apply_retention_policy(self, policy_name):
"""Apply specific retention policy"""
policy = self.policies.get(policy_name, {})
for data_type, days in policy.items():
self.clean_old_data(data_type, days)
def clean_old_data(self, data_type, max_age_days):
"""Remove data older than specified days"""
cutoff = datetime.now() - timedelta(days=max_age_days)
if data_type == 'history':
self.state.history = [
h for h in self.state.history
if datetime.fromisoformat(h['timestamp']) > cutoff
]
elif data_type == 'cache':
cache_dir = os.path.expanduser("~/.naviduck_cache")
for file in os.listdir(cache_dir):
filepath = os.path.join(cache_dir, file)
if os.path.getmtime(filepath) < cutoff.timestamp():
os.remove(filepath)def generate_privacy_policy():
"""Generate a privacy policy based on current settings"""
policy_template = """
Privacy Policy for NaviDuck
Last Updated: {date}
1. Data Collection
==================
NaviDuck collects the following data:
{data_collected}
2. Data Storage
===============
All data is stored locally on your device.
Storage location: {storage_location}
Encryption: {encryption_status}
3. Data Sharing
===============
NaviDuck does not share your data with third parties.
4. Your Rights
==============
You have the right to:
- Access your data
- Delete your data
- Export your data
- Opt-out of data collection
5. Contact
==========
For privacy concerns: {contact_info}
"""
data_collected = []
if len(self.state.history) > 0:
data_collected.append("- Search and browsing history")
if len(self.state.bookmarks) > 0:
data_collected.append("- Bookmarks")
policy = policy_template.format(
date=datetime.now().strftime("%Y-%m-%d"),
data_collected="\n".join(data_collected),
storage_location="~/.naviduck_data.json",
encryption_status="Enabled" if self.encryption_enabled else "Disabled",
contact_info="Open issue on GitHub"
)
with open("naviduck_privacy_policy.txt", "w") as f:
f.write(policy)
print("📄 Privacy policy generated: naviduck_privacy_policy.txt")class PanicButton:
def __init__(self):
self.panic_sequences = {
'kill_switch': 'Ctrl+Alt+K',
'immediate_exit': 'Ctrl+X',
'data_destruct': 'Ctrl+Shift+Delete'
}
def enable_kill_switch(self):
"""Enable emergency kill switch"""
import keyboard # pip install keyboard
def emergency_exit():
print("🚨 EMERGENCY EXIT ACTIVATED")
self.destroy_all_data()
sys.exit(0)
# Register hotkey
keyboard.add_hotkey('ctrl+alt+k', emergency_exit)
print("🔴 Kill switch enabled (Ctrl+Alt+K)")
def destroy_all_data(self):
"""Securely destroy all local data"""
print("🧹 Destroying all local data...")
# Securely delete files
files_to_destroy = [
"~/.naviduck_data.json",
"~/.naviduck_config.json",
"~/.naviduck_cache/",
"~/.navai_cache.json"
]
for file_pattern in files_to_destroy:
filepath = os.path.expanduser(file_pattern)
if os.path.exists(filepath):
if os.path.isdir(filepath):
shutil.rmtree(filepath)
else:
# Overwrite before deleting (simple version)
with open(filepath, 'wb') as f:
f.write(os.urandom(os.path.getsize(filepath)))
os.remove(filepath)
print("✅ All data destroyed")class IncidentResponse:
def __init__(self):
self.incident_log = []
def handle_incident(self, incident_type, severity):
"""Handle security/privacy incidents"""
responses = {
'data_breach': self.handle_data_breach,
'malware_detected': self.handle_malware,
'tracking_detected': self.handle_tracking,
'legal_request': self.handle_legal_request
}
if incident_type in responses:
responses[incident_type](severity)
# Log incident
self.log_incident(incident_type, severity)
def handle_data_breach(self, severity):
"""Respond to potential data breach"""
if severity == 'high':
print("🚨 HIGH SEVERITY DATA BREACH DETECTED")
print("1. Disconnecting from network...")
# Would implement network disconnect
print("2. Securing local data...")
self.encrypt_all_data()
print("3. Notifying user...")
self.notify_user("Data breach detected. Take immediate action.")
def create_incident_report(self):
"""Generate incident report"""
report = f"""
Security Incident Report
========================
Date: {datetime.now()}
Total Incidents: {len(self.incident_log)}
Recent Incidents:
{self.format_incident_log()}
Recommendations:
1. Review privacy settings
2. Enable additional protections
3. Consider using Tor
4. Regular security audits
"""
with open("incident_report.txt", "w") as f:
f.write(report)class ForensicResistance:
"""Make forensic analysis difficult"""
def obfuscate_file_timestamps(self):
"""Randomize file timestamps"""
for root, dirs, files in os.walk(os.path.expanduser("~/.naviduck")):
for file in files:
filepath = os.path.join(root, file)
# Set random timestamps
random_time = time.time() - random.uniform(0, 365*24*3600)
os.utime(filepath, (random_time, random_time))
def add_decoy_data(self):
"""Add plausible but fake data"""
fake_searches = [
"weather in london",
"python tutorial",
"news today",
"recipe for pasta",
"how to tie a tie"
]
for search in fake_searches:
self.state.history.append({
'type': 'search',
'query': search,
'timestamp': (datetime.now() -
timedelta(days=random.randint(1, 30))).isoformat(),
'engine': random.choice(['brave', 'ddg'])
})
def implement_plausible_deniability(self):
"""Implement features for plausible deniability"""
# Create hidden volume concept
hidden_data = {
'visible': self.state.history[:10], # First 10 entries visible
'hidden': self.state.history[10:] # Rest hidden
}
# Only reveal hidden data with special password
if self.check_hidden_password():
return hidden_data['visible'] + hidden_data['hidden']
else:
return hidden_data['visible']- Always use Tor for sensitive searches
- Clear history after sensitive sessions
- Verify URLs before visiting
- Use privacy-focused search engines (DuckDuckGo, Brave)
- Regularly audit your privacy settings
- Clear old cache (> 7 days)
- Update blocklists
- Check for privacy leaks using benchmark tool
- Review stored data and delete unnecessary items
- Backup encrypted data to secure location
- Complete privacy audit
- Update NaviDuck to latest version
- Review privacy policy changes
- Test emergency protocols
- Educate yourself on new privacy threats
{
"must_have": {
"default_engine": "ddg",
"tor_enabled": true,
"encrypt_local_data": true,
"strip_tracking_params": true,
"randomize_user_agent": true,
"dns_over_https": true
},
"recommended": {
"first_party_isolation": true,
"circuit_rotation": 300,
"clear_cache_on_exit": false,
"block_known_trackers": true,
"cookie_isolation": true
},
"advanced": {
"use_bridges": false,
"certificate_pinning": true,
"dnssec_validation": true,
"panic_button": true,
"forensic_resistance": true
}
}- Zero-Knowledge Sync - Encrypted cloud sync
- Decentralized Search - Peer-to-peer search index
- AI Privacy Assistant - Real-time privacy recommendations
- Quantum-Resistant Encryption - Post-quantum cryptography
- Behavioral Obfuscation - AI-generated fake traffic
- Hardware Integration - TPM/YubiKey support
- Blockchain Auditing - Immutable privacy logs
- Differential Privacy for search queries
- Homomorphic Encryption for private AI queries
- Secure Multi-Party Computation for private search
- Federated Learning for improving AI without data collection
Last updated: 12/22/2025
Privacy & Security Guide version: 4.0
Remember: Privacy is a journey, not a destination. Regular maintenance and awareness are key to maintaining your digital privacy.
Pro Tip: Enable the privacy dashboard (privacy audit) monthly to track your protection level and identify areas for improvement.
Stay safe and private! 🔒🦆