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Differentially Private Streaming Data Processing at Scale


Core Concepts
The authors design the first differentially private (DP) stream aggregation processing system at scale, called Differential Privacy SQL Pipelines (DP-SQLP), which can handle large-scale industrial workloads.
Abstract
The authors design a scalable DP stream processing system called DP-SQLP that can handle large-scale industrial workloads. Key highlights: DP-SQLP is built using a streaming framework similar to Spark Streaming, and is built on top of the Spanner database and the F1 query engine from Google. The authors make algorithmic advances to address challenges in the streaming setting, including: Designing a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys and scale to one billion keys. Designing a preemptive execution scheme for DP key selection to avoid enumerating all the keys at each triggering time. Using algorithmic techniques from DP continual observation to release a continual DP histogram of user contributions to different keys over the stream length. The authors empirically demonstrate the efficacy of DP-SQLP, obtaining at least 16x reduction in error over meaningful baselines. DP-SQLP is implemented for a streaming differentially private user impressions for Google Shopping, and the streaming DP algorithms are further applied to Google Trends.
Stats
DP-SQLP can handle millions of updates per second from a data stream with billions of distinct keys. At (ε = 6, δ = 10^-9)-DP, DP-SQLP achieves up to 93.9% error reduction and 65x increase in the number of retained keys compared to baselines.
Quotes
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Key Insights Distilled From

by Bing Zhang,V... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2303.18086.pdf
Differentially Private Stream Processing at Scale

Deeper Inquiries

What are some other real-world applications that could benefit from the DP-SQLP system

The DP-SQLP system can be beneficial in various real-world applications where streaming data processing with differential privacy is crucial. One such application is in healthcare, where patient data needs to be analyzed in real-time while ensuring privacy. For example, monitoring patient vitals or analyzing medical records for research purposes can benefit from the DP-SQLP system. Another application is in financial services, where analyzing transaction data for fraud detection or market trends can be done securely with the DP-SQLP system. Additionally, in the field of marketing and advertising, analyzing user behavior and preferences for targeted advertising while maintaining privacy can also leverage the capabilities of DP-SQLP.

How can the DP-SQLP system be extended to handle more complex analytics tasks beyond simple histogram computations

To handle more complex analytics tasks beyond simple histogram computations, the DP-SQLP system can be extended in several ways. One approach is to incorporate machine learning algorithms for predictive analytics while ensuring differential privacy. This can involve training models on streaming data while preserving privacy guarantees. Additionally, the system can be enhanced to support more advanced data transformations and aggregations, such as time series analysis, anomaly detection, and clustering. By integrating more sophisticated algorithms and techniques, the DP-SQLP system can cater to a wider range of analytics tasks without compromising privacy.

What are the potential trade-offs between the privacy guarantees and the computational/storage overhead of the DP-SQLP system

There are potential trade-offs between the privacy guarantees and the computational/storage overhead of the DP-SQLP system. One trade-off is the level of privacy protection versus the amount of noise added to the data. Increasing the privacy guarantees (e.g., reducing ε or δ values) can result in higher levels of noise, impacting the accuracy of the analytics results. Balancing privacy and utility is crucial in such scenarios. Another trade-off is the scalability of the system. As the system handles larger volumes of data and more complex analytics tasks, the computational and storage requirements may increase, potentially affecting performance. Optimizing the system architecture and algorithms is essential to manage these trade-offs effectively.
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