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Differential Privacy for Anomaly Detection: Analyzing the Trade-Off Between Privacy and Explainability

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Explainable Artificial Intelligence (xAI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2155))

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Abstract

Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data. Such a process finds wide application in various fields, such as finance and healthcare. While the primary objective of AD is to yield high detection accuracy, the requirements of explainability and privacy are also paramount. The first ensures the transparency of the AD process, while the second guarantees that no sensitive information is leaked to untrusted parties. In this work, we exploit the trade-off of applying Explainable AI (XAI) through SHapley Additive exPlanations (SHAP) and differential privacy (DP). We perform AD with different models and on various datasets, and we thoroughly evaluate the cost of privacy in terms of decreased accuracy and explainability. Our results show that the enforcement of privacy through DP has a significant impact on detection accuracy and explainability, which depends on both the dataset and the considered AD model.

F. Ezzeddine and M. Saad—Co-first author.

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Notes

  1. 1.

    The summary plots display SHAP values for each feature and data point, indicating their impact on classifying normal or abnormal. On the x-axis, SHAP values show a feature’s influence on predictions, with positive or negative values indicating a tendency towards an abnormal or normal prediction, respectively. The y-axis ranks features by importance, and point colors signify feature values-red for high and blue for low.

  2. 2.

    As similar trends in the SHAP summary plots are observed in the two other datasets, we omit to show them and the relative discussion. To illustrate the visual changes, we show summary plots for only two \(\varepsilon \) values (0.01 and 5).

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Acknowledgements

F. Ezzeddine was supported by the Swiss Government Excellence Scholarship. Dr. M. Gjoreski’s work was funded by SNSF through the project XAI-PAC (grant number PZ00P2_216405).

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Authors and Affiliations

  1. University of Applied Sciences and Arts of Southern Switzerland, Manno, Switzerland

    Fatima Ezzeddine, Omran Ayoub, Davide Andreoletti & Silvia Giordano

  2. Università della Svizzera italiana, Lugano, Switzerland

    Fatima Ezzeddine, Martin Gjoreski & Marc Langheinrich

  3. Lebanese University, Beirut, Lebanon

    Mirna Saad & Ihab Sbeity

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  1. Fatima Ezzeddine
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Correspondence to Fatima Ezzeddine .

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  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Fraunhofer Institute for Telecommunications, Berlin, Germany

    Sebastian Lapuschkin

  3. University of Marburg, Marburg, Germany

    Christin Seifert

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Ezzeddine, F. et al. (2024). Differential Privacy for Anomaly Detection: Analyzing the Trade-Off Between Privacy and Explainability. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2155. Springer, Cham. https://doi.org/10.1007/978-3-031-63800-8_15

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  • DOI: https://doi.org/10.1007/978-3-031-63800-8_15

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