Abstract
This research investigates methods for protecting user privacy against modern tracking techniques, including browser fingerprinting, traffic analysis, and behavioral profiling. We study the effectiveness of various defense mechanisms and their impact on web service functionality, with the goal of developing approaches that provide strong privacy guarantees while maintaining acceptable user experience.
The Problem
Pervasive Online Tracking
Modern web tracking has evolved far beyond traditional cookies. Sophisticated techniques now enable persistent identification of users across browsing sessions, devices, and even when privacy tools are employed. These methods operate silently, often without user awareness or meaningful consent mechanisms.
Browser Fingerprinting
Websites can collect dozens of attributes from a user's browser and device—including screen resolution, installed fonts, graphics card behavior, and timezone—to construct a unique identifier. Research has shown that the combination of these attributes is often sufficient to uniquely identify individuals among millions of users.
Traffic Analysis
Even when content is encrypted, the patterns of network traffic—packet sizes, timing, and flow characteristics—can reveal information about user behavior, visited websites, and online activities.
Behavioral Profiling
The way users interact with websites—typing patterns, mouse movements, scroll behavior—creates distinctive signatures that can be used for identification and tracking across sessions.
The Privacy-Utility Tradeoff
Existing defenses often come with significant costs. Blocking JavaScript breaks many websites. Uniformly spoofing all browser attributes can degrade functionality or create detectable anomalies. The challenge lies in finding protection methods that effectively defend against tracking while preserving the utility users expect from the web.
Threat Models Under Study
Fingerprinting Attacks
We study machine learning-based classifiers that attempt to identify users from browser and device characteristics. These attacks assume an adversary who has collected fingerprints during previous visits and seeks to re-identify returning users.
Cross-Site Correlation
We examine techniques that attempt to link user activity across different websites by analyzing shared characteristics, timing patterns, or behavioral signatures.
Traffic Analysis
We investigate statistical methods that analyze encrypted network traffic to infer user behavior, website visits, or to correlate activity across sessions.
Shadow Profiling
We study attacks that build user profiles indirectly through behavioral patterns such as typing dynamics, interaction timing, and navigation behavior.
Evaluation Criteria
We evaluate privacy protection methods across multiple dimensions:
| Criterion | Description |
|---|---|
| Protection Effectiveness | Reduction in attack success rates compared to unprotected baseline |
| Service Compatibility | Percentage of web services that function correctly under protection |
| Performance Impact | Additional latency or resource usage introduced by protection |
| Detectability | Whether the protection mechanism itself can be detected by adversaries |
| Adaptability | Resilience against evolving attack techniques |
Research Phases
Phase 1: Baseline Measurement Complete
Establish baseline measurements for tracking attack effectiveness. Characterize the feature spaces used by various tracking techniques.
Phase 2: Vulnerability Analysis Complete
Analyze which data points and patterns contribute most to successful tracking attacks.
Phase 3: Defense Development Complete
Develop and implement candidate protection mechanisms based on analysis findings.
Phase 4: Evaluation In Progress
Comprehensive evaluation of protection effectiveness, utility preservation, and performance characteristics.
Phase 5: Documentation Pending
Prepare findings for publication and peer review.
Contact
This research is ongoing. For inquiries regarding collaboration or access to findings, please contact the research team.