AntiMalwareJuly 10, 2026🇷🇺Translated from Russian

Windows Tracks Users Through Persistent GDID Identifier: How to Minimize Your Digital Footprint

The case of a 19-year-old hacker who was tracked down using his Windows GDID identifier has once again demonstrated that Microsoft maintains a persistent device fingerprint that is difficult to erase. Even when users rely on VPNs or frequently change IP addresses, the underlying Windows installation remains recognizable to Microsoft services through this unique identifier.

What is GDID and Why It Matters

GDID is a permanent device identifier used across Microsoft’s ecosystem, including licensing verification, the Microsoft Store, telemetry collection, and various cloud services. Because it is tied directly to the Windows installation, it survives network changes and creates a stable link between a user’s device and their activity history.

Practical Steps to Reduce Tracking

Although Microsoft does not provide a single switch to disable GDID entirely, users can take several concrete measures to limit data exposure:

  • Use a local account instead of a Microsoft Account. Signing in with a Microsoft Account strengthens the connection between the device, OneDrive, the Store, and activity history. A local profile remains significantly more private.
  • Disable activity history. Navigate to Settings → Privacy & security → Activity history and turn off the option to store activity history. This may affect features such as cross-device synchronization and the cloud clipboard, but it reduces the data sent to Microsoft.
  • Limit diagnostic and feedback data. In Settings → Privacy & security → Diagnostics & feedback, disable the sending of optional diagnostic data. While basic telemetry cannot be fully removed, this step prevents the transmission of non-essential information.
  • Disable unused background services. Turn off Phone Link, Nearby Share, cloud synchronization, unnecessary AI features, and any startup programs that connect to Microsoft servers. The fewer connections to the Microsoft ecosystem, the smaller the digital footprint.

It is important to note that reinstalling Windows does not automatically solve the problem. While a fresh installation generates a new GDID, signing back into the same Microsoft Account allows Microsoft to link the new installation with previous ones through account activity, license activation, and service history.

Related articles

HabrPrivacy & Surveillance

Amnezia VPN Survives Coordinated Russian Censorship Campaign Targeting AmneziaWG Protocol Fingerprints

Amnezia VPN has published a detailed post-mortem on the multi-wave blocking campaign conducted by Russian authorities against its Amnezia Free and Amnezia Premium services during June and July. The company describes a shift from simple protocol blocking to sophisticated fingerprinting of AmneziaWG traffic combined with infrastructure DDoS attacks and automated IP-subnet blacklisting. Engineers closed multiple detection vectors including zero-length UDP packets, fixed-size keepalive messages, handshake timing patterns, and nonce zero bytes. The incident forced accelerated migration to AmneziaWG 2.0, discontinuation of legacy client support, and development of AmneziaWG 3.0 while expanding VLESS infrastructure as a backup. Self-hosted users largely avoided direct protocol blocks but still faced subnet-level restrictions. The report highlights how Roskomnadzor now applies cumulative scoring across multiple traffic features rather than single definitive markers.

HabrPrivacy & Surveillance

Data Masking: 8 Critical Questions Businesses and Developers Ask About Protecting Sensitive Data

Garda expert Dmitry Larin addresses common challenges in data masking during a recent webinar titled 'Data Masking: Battle of Opinions'. The discussion covers why masking remains essential even when encryption is deployed, how to preserve application functionality after anonymization, and the performance trade-offs of processing large databases such as 5 TB PostgreSQL instances. Different masking types including static, dynamic, selective, and streaming are explained with specific use cases for DevOps pipelines, external contractors, and BI systems. The article also examines why machine learning alone is insufficient for discovering personal data and why custom scripts fail at scale across heterogeneous environments like PostgreSQL and Oracle. Practical recommendations include combining masking with encryption, using deterministic transformations for deduplication, and separating replication from masking tasks to avoid production impact.

AntiMalwarePrivacy & Surveillance

MAX Desktop Client Tested for VPN Detection on Windows, No Tracking Signs Found

A Habra user named Slava_B conducted an experiment on September 8, 2026, to determine whether the MAX desktop client on Windows could detect or route traffic through a VPN configured at the router level. The setup used a Keenetic router that directed Russian resources directly while sending other connections via an OpenConnect tunnel to a European VPS, with no VPN client or virtual adapter present in Windows itself. Monitoring tools including Process Monitor, Wireshark, TCPView, and tcpdump revealed that MAX.exe and MAX-service.exe processes communicate locally and connect to MAX/ONEME infrastructure along with AppTracer services. The application repeatedly accessed MachineGuid, computer name, proxy settings, device IDs, and microphone/camera information, though these reads may support diagnostics and anti-fraud functions. No connections appeared on the VPN interface, and the client did not attempt to reach IP-checking services, Telegram, or WhatsApp. The researcher noted that TLS traffic was not decrypted, so actual transmission of identifiers could not be confirmed, and results apply only to this router-based configuration.

HabrPrivacy & Surveillance

PII-Guard: Open-Source Detector for Personal Data in Russian Text

Andrey Ivanov, an NLP researcher at red_mad_robot, has released PII-Guard, an open-source system that detects and masks personal data in Russian text before it reaches language models. The tool combines rule-based checks with a fine-tuned ruBert-base NER model to handle names, addresses, phones, passports, INN, SNILS, bank cards and other entities. It replaces detected PII with structured XML-like tags that preserve grammatical information such as gender and entity ID, allowing models to generate coherent responses that are later restored with real values. The hybrid pipeline first applies normalization, pattern matching, Luhn and weighted checksum validation, and context windows with positive and negative keywords, then merges results with model predictions via an arbitration module. Evaluation on four public datasets, including Hivetrace, alexen2 and alrosait, shows PII-Guard outperforming other open solutions on both strict span matching and type-overlap micro-F1 metrics. The project, including datasets and code, is available on GitHub and aims to reduce leakage risks while maintaining downstream model utility.