Privacy-Preserving Machine Learning by Srinivas Rao Aravilli
Author:Srinivas Rao Aravilli
Language: eng
Format: epub
Publisher: Packt Publishing Pvt Ltd
Published: 2024-04-29T00:00:00+00:00
Figure 5.12 â Transactions aggregates
By utilizing the PipelineDP framework, we can address the challenges and considerations involved in generating differentially private aggregates for large datasets. It provides a comprehensive solution that combines scalability, privacy preservation, and accurate aggregation, allowing us to effectively leverage differential privacy techniques for large-scale data analysis.
Tumult Analytics
Tumult Analytics is a robust and feature-rich Python library designed for performing aggregate queries on tabular data while ensuring the principles of differential privacy. The library offers an intuitive interface, making it accessible to users familiar with SQL or PySpark. It provides a wide range of aggregation functions, data transformation operators, and privacy definitions, ensuring versatility in analytical tasks. Developed and maintained by a team of experts in differential privacy, Tumult Analytics guarantees reliability and is even utilized in production environments by reputable institutions such as the U.S. Census Bureau. Powered by Spark, the library demonstrates excellent scalability, enabling efficient processing of large datasets. With its comprehensive functionality and emphasis on privacy preservation, Tumult Analytics is a valuable tool for data analysis with a focus on maintaining data privacy.
The following is the citation for the Tumult Analytics open source framework:
@software{tumultanalyticssoftware, author = {Tumult Labs}, title = {Tumult {{Analytics}}}, month = dec, year = 2022, version = {latest}, url = {https://tmlt.dev} }
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