# SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

arXiv: https://arxiv.org/abs/2505.14976 · HTML: https://mlsanity.com/paper/2505.14976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 115 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [LILAC: Log Parsing using LLMs with Adaptive Parsing Cache](https://mlsanity.com/paper/2310.01796/) (2023)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)
- [Biomedical named entity recognition using BERT in the machine reading comprehension framework](https://mlsanity.com/paper/2009.01560/) (2020)
- [Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction](https://mlsanity.com/paper/2304.11391/) (2023)
- [Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs](https://mlsanity.com/paper/2404.00640/) (2024)
- [Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback](https://mlsanity.com/paper/2311.07215/) (2023)
- [Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper](https://mlsanity.com/paper/2101.06054/) (2021)
- [Hue: A User-Adaptive Parser for Hybrid Logs](https://mlsanity.com/paper/2308.07085/) (2023)
- [Studying the Characteristics of AIOps Projects on GitHub](https://mlsanity.com/paper/2212.13245/) (2022)
- [GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection](https://mlsanity.com/paper/2309.05953/) (2023)
- [Unlocking the Power of Numbers: Log Compression via Numeric Token Parsing](https://mlsanity.com/paper/2408.05760/) (2024)
- [Protecting Privacy in Software Logs: What Should Be Anonymized?](https://mlsanity.com/paper/2409.11313/) (2024)
- [Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization](https://mlsanity.com/paper/2409.12299/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs](https://mlsanity.com/paper/2604.20211/) (2026)
- [Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation](https://mlsanity.com/paper/2603.29878/) (2026)

## Abstract (arXiv)

Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a critical role in software maintenance and operation tasks, publicly accessible log datasets remain limited, hindering advance in log analysis research and practices. The presence of sensitive information, particularly Personally Identifiable Information (PII) and quasi-identifiers, introduces serious privacy and re-identification risks, discouraging the publishing and sharing of real-world logs. In practice, log anonymization techniques primarily rely on regular expression patterns, which involve manually crafting rules to identify and replace sensitive information. However, these regex-based approaches suffer from significant limitations, such as extensive manual efforts and poor generalizability across diverse log formats and datasets. To mitigate these limitations, we introduce SDLog, a deep learning-based framework designed to identify sensitive information in software logs. Our results show that SDLog overcomes regex limitations and outperforms the best-performing regex patterns in identifying sensitive information. With only 100 fine-tuning samples from the target dataset, SDLog can correctly identify 99.5% of sensitive attributes and achieves an F1-score of 98.4%. To the best of our knowledge, this is the first deep learning alternative to regex-based methods in software log anonymization.