Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy

Abstract: Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, while novelties refer to new samples. In the meantime, backdoor poisoning attacks for machine learning models are achieved through injecting poisoning samples into the training dataset, which could be regarded as "outliers" that are intentionally added by attackers. Differential privacy has been proposed to avoid leaking any individual's information, when aggregated analysis is performed on a given dataset. It is typically achieved by adding random noise, either directly to the input dataset, or to intermediate results of the aggregation mechanism. In this paper, we demonstrate that applying differential privacy can improve the utility of outlier detection and novelty detection, with an extension to detect poisoning samples in backdoor attacks. We first present a theoretical analysis on how differential privacy helps with the detection, and then conduct extensive experiments to validate the effectiveness of differential privacy in improving outlier detection, novelty detection, and backdoor attack detection.

• The paper proposes a novel differential privacy framework to detect anomalies and backdoor attacks in machine learning systems.
• It employs privacy-preserving techniques to robustly analyze model behavior, ensuring accurate attack identification even under adversarial conditions.
• Experimental results demonstrate improved robustness and reliability, underscoring the framework's potential for securing advanced ML applications.

Summary of "Formatting Instructions For NeurIPS 2019"

This document, authored by David S. Hippocampus, serves as the official formatting guide for paper submissions to the NeurIPS 2019 conference. It emphasizes the rigorous adherence to submission requirements outlined by the conference committee. The paper meticulously details the structural and stylistic criteria that submissions must meet, focusing heavily on the LaTeX setup preferred for consistency and readability.

The document establishes the requirement for submissions to be no longer than eight pages of content, excluding references and acknowledgments. This aligns with the conference's objective to ensure concise and focused dissemination of findings. Authors are reminded that non-compliance with the eight-page limit results in automatic rejection without review, highlighting the importance of precision in addressing key research insights.

A significant segment is dedicated to the technical details of LaTeX style files, which have undergone optimization since 2007. With the prescribed style offering approximately a 15% increase in word count capacity compared to prior conference years, authors are urged to utilize the enhanced format for improved content density. The document restricts the use of previous formatting styles, indicating the shift towards uniform yet flexible document rendering which supports both peer review processes and open accessibility channels like arXiv.

The instructions incorporate guidelines on appropriate typographic considerations, such as font usage, specifically Type 1 or Embedded TrueType fonts, to avoid formatting issues upon submission. This technical specification illustrates the conference's commitment to a standardized visual output that minimizes reproduction errors.

Hippocampus' document also provides directives on graphical and tabular content, stipulating clarity and legibility requirements that ensure compatibility across various viewing formats. This attention to detail extends to the proper integration and citation of references, with prescriptions for consistent citation styles enforceable through the natbib LaTeX package.

The implications of this document are primarily operational, providing a foundation for submissions that align with peer-reviewed academic standards. It underscores the organizational rigor underpinning large-scale conferences, where uniform presentation supports both the review process and subsequent archival in academic repositories.

In speculating on future developments, these guidelines could serve as a template for evolving submission standards that incorporate advancements in collaborative writing tools and publishing platforms. As AI and machine learning research proliferate, conferences such as NeurIPS may further refine formatting instructions to facilitate the integration of interactive and multi-modal research presentations, expanding the conventional boundaries of academic publishing.