How can we identify users without relying on cookies or local storage?

User Recognition Without Cookies or Local Storage

Introduction:

Detecting users without cookies or local storage is a complex task due to the ephemeral nature of these identifiers. However, there are various techniques and strategies that can be employed to approach this challenge.

Data Profile Generation:

The first step is to gather a comprehensive data profile for each user. This profile should include a combination of the following data points:

• IP Address: Both real and proxy IP addresses
• Browser Fingerprint: Unique combination of browser and OS settings
• Installed Plugins: Plugins installed in the user's browser
• Font Fingerprints: Distinctive characteristics of system fonts
• Geolocation: User's estimated location
• Encrypted URL History: Anonymized URLs visited by the user
• Session Cookies: Short-lived cookies that are tied to a specific browser session
• HTML5 Local Storage: Data stored locally in the browser
• Device Information: Device type, operating system, and screen resolution

Probabilistic or AI-Based Detection:

Once a detailed data profile has been established, various techniques can be applied to identify users probabilistically or using artificial intelligence:

1. Probability Scoring:

Each data point in the profile is assigned a weight and importance score. When a new user is encountered, their data points are compared to the profiles of known users. A score is calculated based on the number and weight of matching points. The user with the highest score is considered the most likely match.

2. Artificial Neural Network (ANN):

An ANN is a machine learning model that can be trained using a dataset of known user profiles. Once trained, the ANN can classify new users based on their data profiles and generate a list of possible matches.

3. Fuzzy Logic:

Fuzzy logic deals with uncertain and imprecise information. It can be used to combine multiple data points and their associated weights to determine the probability of a match.

4. Bayesian Inference:

Bayesian inference is a statistical technique that combines prior knowledge with new data to update probabilities. It can be used to estimate the likelihood that a new user is a known user or a new user.

Limitations and Considerations:

• Privacy Concerns: Gathering such extensive data raises privacy concerns. Users must be informed and consent to the collection and use of their data.
• Accuracy: The accuracy of user detection depends on the completeness and accuracy of the data profile and the efficiency of the detection algorithm.
• Device Changes: Changes in the user's device or network can result in changes to their data profile, potentially affecting the accuracy of user detection.
• Browser Extensions and Ad Blockers: Users may employ browser extensions or ad blockers that can interfere with data collection.

Conclusion:

User recognition without cookies or local storage is a challenging task that requires a comprehensive data profile and sophisticated detection techniques. By combining probability, AI, and fuzzy logic approaches, it is possible to develop probabilistic matches with varying degrees of accuracy. However, privacy concerns and the dynamic nature of user data must be carefully considered and addressed to implement such solutions ethically and effectively.

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