Digital Fingerprinting explained: applications, benefits, and privacy-friendly alternatives

Tracking technologies have evolved significantly, reshaping the way businesses and organizations monitor user behavior online. In today’s digital landscape, traditional methods like cookies face increasing challenges, including privacy concerns and regulatory restrictions (Pérez & Huang, 2023). As a result, new, privacy-preserving tracking methods are gaining prominence. Among these, device fingerprinting and AI-based solutions such as synthetic users offer innovative approaches to understanding online activity without compromising user privacy (GDPR.eu, 2023). This article explores device fingerprinting, its workings, applications, and the alternatives, emphasizing their role in AI tracking.

What is Device Fingerprinting?

Device fingerprinting is a modern tracking method that identifies and distinguishes users based on the unique characteristics of their devices and online behavior. Unlike traditional cookie-based tracking, device fingerprinting does not rely on stored data on the user’s device. Instead, it collects information about the browser, device configuration, and interaction patterns to create a unique, anonymized “fingerprint” (Akamai Technologies, 2023). However, some modern browsers have implemented mechanisms specifically designed to disrupt fingerprinting. They alter or obscure certain characteristics, such as rendering properties, or simulate random values, making this method less reliable.

Key Components of Device Fingerprinting

Device Characteristics
Information like operating system, screen resolution, browser version, and installed plugins helps create a unique user profile. These details are typically consistent across a user’s online sessions, making them reliable identifiers (Liu et al., 2024).

Behavioral Data
Behavioral data includes subtle, measurable traits such as mouse movements, typing speed, and click patterns. These human-centric features add another layer of uniqueness (Akamai Technologies, 2023).

Together, these elements create a comprehensive fingerprint that is nearly impossible to replicate, allowing companies to track users without relying on traditional tracking cookies. However, there are increasing technical countermeasures. Privacy-focused browsers like Firefox and Safari actively reduce the visibility of certain device parameters or standardize differences between users to make tracking more difficult. As a result, creating accurate fingerprints becomes more challenging, as the collected data may be incomplete or distorted.

Step-by-Step Process

1. Data Collection:
   Websites or applications extract detailed information such as browser type, installed plugins, operating system, screen resolution, and even GPU performance. These attributes are inherently unique and persist across browsing sessions (Liu et al., 2024).
2. Behavioral Profiling:
   Subtle aspects like typing speed, mouse movements, and scrolling patterns are tracked to further individualize the fingerprint. These behavioral traits are nearly impossible to duplicate, adding a deeper layer of precision (Akamai Technologies, 2023).
3. Aggregation and Hashing:
   The collected data is aggregated into a “hash” or cryptographically encoded identifier. This encoding helps anonymize the fingerprint and aligns it with global privacy regulations like GDPR and CCPA (Pérez & Huang, 2023).
4. Integration with AI Models:
   Fingerprinting systems increasingly incorporate AI to refine identification accuracy and detect patterns such as bot activities. AI enhances the scalability of fingerprinting for use in fraud detection, cybersecurity, and ad targeting (Liu et al., 2024).

Key Strengths

Passive and invisible: Fingerprinting operates silently without user intervention or cookies, making it difficult for users to detect or block.

Cross-Device and durable: Unlike cookies, which are device-specific and deletable, fingerprints are robust across multiple devices and sessions.

Resilience to privacy features: It can persist even when users activate privacy settings or reject tracking, raising questions about regulatory compliance (Liu et al., 2024). However, many modern browsers are increasingly designed to disrupt this type of tracking. Safari and Firefox, for instance, use “anti-fingerprinting” technologies that randomly modify or hide certain device information to prevent unique identification. As a result, not all fingerprinting attempts are equally effective, and the longevity of this method is challenged by such technical barriers.

Privacy Concerns

While fingerprinting is invaluable for cybersecurity and fraud prevention, its use in online advertising often encroaches on user privacy. Research indicates that advertisers exploit fingerprinting to bypass tracking consent, tailoring bids based on a user’s device footprint (Liu et al., 2024). 

Applications and Use Cases of Device Fingerprinting

Device fingerprinting has emerged as a versatile tool in various industries, its applications extend beyond marketing, offering solutions for security, personalization, and compliance.

Fraud Detection and Cybersecurity

Device fingerprinting is widely used in fraud prevention and cybersecurity, helping businesses detect and block suspicious activities. By analyzing device configurations and behavioral patterns, it can differentiate between genuine users and automated bots (Liu et al., 2024). In the financial sector, fingerprinting flags anomalies such as logins from unfamiliar devices or locations, enhancing security against phishing and account takeovers.

Personalized Marketing and Advertising

In marketing, device fingerprinting enables cookie-less user segmentation and tailored advertising. Advertisers use fingerprints to identify user preferences and adjust campaigns in real-time. Programmatic advertising, for instance, relies on fingerprinting to optimize bidding strategies (Pérez & Huang, 2023). Platforms also use it to recommend relevant content and improve user engagement.

User Authentication

Fingerprinting enhances security in authentication systems by adding a unique identifier as an additional verification layer. This is particularly valuable in industries handling sensitive data, such as healthcare and software-as-a-service (SaaS) platforms. By ensuring that only authorized users access confidential systems, businesses strengthen their defenses against unauthorized access.

Compliance Monitoring

Fingerprinting helps ensure compliance with subscription terms or licensing agreements by tracking unique user sessions and preventing account sharing.

Customer Journey Mapping

Businesses use fingerprinting to track how users navigate websites, enabling better optimization of user interfaces and customer experience without the need for cookies.

AI-Based Alternatives to Device Fingerprinting

As privacy regulations tighten and ethical concerns grow, organizations are turning to AI-powered tracking solutions that provide valuable insights without compromising user privacy. These alternatives use advanced technologies such as synthetic data, federated learning, and contextual AI to provide ethical, accurate, and compliant tracking.

Synthetic User Data Solutions (e.g., JENTIS Synthetic Users)

Synthetic user data is a transformative solution, with JENTIS Synthetic Users leading the field. This technology addresses significant challenges posed by data loss from user opt-outs and tracking restrictions. By combining consented user data with statistical modeling, JENTIS generates synthetic profiles that mimic real user behavior without exposing personal information (JENTIS, 2023). This process involves machine learning algorithms imputing missing data for users who do not consent to tracking, allowing marketers to analyze 100% of website traffic.

Synthetic profiles maintain privacy while enabling integration into analytics tools like Google Analytics and advertising platforms such as Google Ads. This approach ensures businesses can optimize campaigns and customer engagement without breaching user privacy laws.

Federated Learning-Based Tracking

Federated learning decentralizes data processing, keeping user data on individual devices while sharing aggregated insights with a central system. This approach enhances privacy while supporting personalized services, such as app recommendations. Federated learning is widely used in platforms like Google’s Gboard, where it ensures user privacy while delivering tailored suggestions (Kairouz et al., 2019).

Differential Privacy Algorithms

Differential privacy adds noise to datasets, anonymizing individual data points while retaining aggregated insights. This method, adopted by companies like Apple and Google, ensures that datasets cannot be reverse-engineered to identify individuals. It is especially valuable in industries such as healthcare, where privacy is critical (Apple, 2021; Dwork & Roth, 2014).

Contextual AI for Ad Targeting

Contextual AI eliminates the need for user-based tracking by analyzing webpage content and delivering ads based on context rather than user profiles. This approach is compliant with privacy laws and effective for marketers who prioritize content-driven strategies (Saura et al., 2024).

References

Apple. (2021). Differential privacy overview. Retrieved from https://apple.com/privacy

Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042

JENTIS. (2023). AI-based tracking: Synthetic user. Retrieved from https://www.jentis.com

Kairouz, P., McMahan, H. B., & others. (2019). Advances and open problems in federated learning. arXiv preprint. https://doi.org/10.48550/arXiv.1912.04977

Liu, Z., Dani, J., Wu, S., Cao, Y., & Saxena, N. (2024). Identified-and-targeted: The first early evidence of the privacy-invasive use of browser fingerprinting for online tracking. arXiv preprint. https://doi.org/10.48550/arXiv.2409.15656

Pérez, A., & Huang, K. (2023). Digital tracking trends in 2023: A privacy-first approach. Retrieved from https://gdpr.eu

Saura, J. R., Škare, V., & Došen, Đ. O. (2024). Is AI-based digital marketing ethical? Assessing a new data privacy paradox. Journal of Innovation & Knowledge, 9(1), 100597. https://doi.org/10.1016/j.jik.2024.100597

Akamai Technologies. (2023). What is digital fingerprinting and how does it work? Retrieved from https://www.akamai.com