Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling

核心概念

The author presents a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis to maintain performance prediction accuracy. The main thesis is that synthetic training data can be used effectively to preserve privacy while maintaining model accuracy.

摘要

The content discusses the challenges in performance modeling for large-scale data analytics workloads and the need for significant amounts of training data. It introduces a privacy-preserving method using differential privacy and data synthesis, showing that fully anonymized training data can maintain performance prediction accuracy. The evaluation on 736 Spark job executions indicates only a one percent reduction in performance model accuracy with synthetic training data when original data samples are limited. Various approaches to collaborative machine learning privacy are explored, highlighting the effectiveness of obfuscation techniques like Data Synthesis. The paper outlines an automated method for collaborative performance modeling while preserving privacy, emphasizing the importance of maintaining accurate relations between execution context and runtime. Experimental results demonstrate the feasibility of generating synthetic training data without compromising model accuracy, especially beneficial when original data points are scarce.

統計資料

Performance models require substantial amounts of training data.
Evaluation on 736 Spark job executions.
Synthetic training data resulted in a one percent reduction in performance model accuracy.
Differential privacy and data synthesis used for sharing runtime metrics.
Overhead measured on typical consumer hardware ranges from half a second to ten seconds.

引述

"We present a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis."
"Our evaluation on performance data from 736 Spark job executions indicates that fully anonymized training data largely maintains performance prediction accuracy."

從以下內容提煉的關鍵洞見

Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling

by Jonathan Wil... 於 arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05692.pdf

深入探究

How can differential privacy methods be improved to enhance collaborative machine learning?

Differential privacy methods can be enhanced in several ways to improve collaborative machine learning. One approach is to focus on refining the noise addition mechanisms used in differential privacy. By optimizing the noise generation process, researchers can reduce the impact of added noise on model accuracy while still preserving individual data privacy.
Additionally, exploring advanced techniques such as local differential privacy, where noise is added at the individual data level before aggregation, could provide a more granular and nuanced approach to protecting sensitive information. This method allows for greater control over the amount of noise introduced and offers increased flexibility in balancing privacy and utility.
Furthermore, incorporating secure multi-party computation (MPC) protocols into differential privacy frameworks can enable multiple parties to jointly analyze data without revealing their inputs. MPC ensures that computations are performed securely across distributed datasets while maintaining strict confidentiality.
In summary, by fine-tuning noise addition strategies, leveraging local differential privacy approaches, and integrating MPC protocols, researchers can enhance the effectiveness and efficiency of differential privacy methods for collaborative machine learning tasks.

What are the potential drawbacks or limitations of using synthetic training data in performance modeling?

While synthetic training data offers benefits such as preserving data privacy and enabling collaboration among organizations with sensitive datasets, there are several potential drawbacks and limitations associated with its use in performance modeling:

Loss of Real-World Variability: Synthetic data may not fully capture all nuances present in real-world datasets. The generated samples might lack certain patterns or outliers that could significantly impact model performance when applied to actual scenarios.

Overfitting Risks: Models trained solely on synthetic data run the risk of overfitting to artificial patterns present only in the generated samples. This could lead to poor generalization when deployed on authentic datasets.

Bias Introduction: The process of synthesizing data may inadvertently introduce biases based on assumptions made during generation or inherent biases within the original dataset used for synthesis. These biases can skew model predictions and compromise fairness.

Scalability Challenges: Generating large volumes of high-quality synthetic training data can be computationally intensive and time-consuming, especially for complex models or extensive feature spaces.

Evaluation Difficulty: Assessing the quality and representativeness of synthetic training data poses challenges since there may not always be clear metrics or benchmarks available for comparison against real-world ground truth.

6 .Interpretability Concerns: Models trained on purely synthetic datasets may lack interpretability since they operate based on artificially created features rather than meaningful real-world relationships between variables.

How might advancements in obfuscation techniques impact future collaborative machine learning?

Advancements in obfuscation techniques hold significant promise for shaping future developments in collaborative machine learning:
1 .Enhanced Data Privacy: Improved obfuscation methods will allow organizations to share sensitive information without compromising individual user confidentiality or proprietary business insights.
2 .Increased Collaboration: With robust obfuscation techniques ensuring secure sharing mechanisms, organizations will feel more confident collaborating on joint projects involving confidential datasets.
3 .Regulatory Compliance: Advanced obfuscation tools align with stringent regulatory requirements regarding user consent and personal information protection (e.g., GDPR), facilitating compliance within cross-organizational collaborations.
4 .Model Fairness: By applying sophisticated obfuscation algorithms that mitigate bias introduction during shared dataset processing stages,
future collaborative ML endeavors stand a better chance at producing fairer models that uphold ethical standards.
5 .Efficient Resource Allocation: Streamlined processes enabled by cutting-edge obfuscation technologies streamline resource allocation decisions
across participating entities involved in joint ML initiatives
6 .Trust Building: As trust forms a cornerstone element essential for successful collaboration,
advancementsinobfuscatiotechniquesplayacriticalroleinstrengtheningtrustamongparticipatingorganizationsandpromotinglong-termpartnerships