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    Area of Science:

    • Computer Science
    • Data Visualization
    • Privacy-Preserving Technologies

    Background:

    • Differential Privacy (DP) is a robust privacy model increasingly used across domains.
    • DP protects individual data by adding calibrated noise, preserving aggregate statistics.
    • Visualizing DP data offers privacy but may distort patterns, impacting analysis utility.

    Purpose of the Study:

    • To investigate the effectiveness of private visualizations for data exploration and analysis.
    • To understand the challenges and opportunities in using visual data analysis with DP.
    • To empirically evaluate user performance across different privacy levels, tasks, and visualization types.

    Main Methods:

    • A crowdsourced experiment was conducted with participants performing analysis tasks.
    • Three privacy levels (high, low, non-private) were tested.
    • Eight analysis tasks and four visualization types (bar, pie, line, scatter) were evaluated.

    Main Results:

    • Participant accuracy was higher for summary tasks (e.g., finding clusters) than value retrieval tasks.
    • Under DP, pie charts and line charts showed comparable or superior accuracy to bar charts.
    • A dichotomous model for measuring user success under DP was developed.

    Conclusions:

    • The study provides empirical evidence on the task-based effectiveness of basic private visualizations.
    • Findings suggest specific visualization types and task categories perform differently under DP.
    • Distribution metrics were proposed for tuning noise injection to enhance DP visualization utility.