How Can We Identify Users Online Without Cookies or Local Storage?

User Recognition Without Cookies or Local Storage

Introduction

Identifying users online can be challenging, especially without relying on cookies or local storage. Here's an exploration of potential approaches to address this problem:

Building a Data Profile

Without unique identifiers, we need to create a "Data Profile" that uniquely characterizes a user. This profile can be constructed by collecting the following data points:

• Network: IP address, proxy IP address, user agent, browser fingerprinting
• Interaction: Click tracking, stored images, URL patterns, system fonts
• Device: Architecture, OS language, resolution, hardware information

Data Analysis Techniques

Once the data profile is established, various analysis techniques can be employed to identify users.

• Fuzzy Logic: Models human reasoning to determine the probability of a match.
• Artificial Neural Network (ANN): Uses a mathematical model to learn and recognize patterns in the data.

To enhance accuracy, Bayesian Inference can be used to combine the results of multiple analyses.

Practical Considerations

• Privacy: Secure storage and handling of user data is essential.
• Cross-browser Compatibility: The solution should work across different browsers.
• Scalability: Handling large volumes of data can be challenging.

Alternative Approaches

• Device Fingerprinting: Uses hardware-based information to associate multiple sessions with a single device.
• Flash Cookies (LSOs): Can be persistent and difficult to delete, offering potential for long-term tracking.
• HTML5 Local Storage and Geolocation: Allow for more granular identification within a single browser session.

Conclusion

Identifying users without cookies or local storage is a complex challenge. By combining multiple data sources, using advanced data analysis techniques, and considering practical implications, it's possible to develop robust solutions that improve user recognition accuracy.

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