The Beauty of (Big) Data Privacy Engineering

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Privacy engineering is an emerging discipline within the software and data engineering domains aiming to provide methodologies, tools, and techniques such that the engineered systems provide acceptable levels of privacy. In this talk, I will present our recent work on anonymization and privacy preserving analytics on large scale geo location datasets. In particular, the focus is on how to scale anonymization and geospatial analytics workloads with Spark, maximizing the performance by combining multi-dimensional spatial indexing with Spark in-memory computations.

Datos y análisis

The Beauty of (Big) Data
Privacy Engineering
Yangcheng Huang
Director of Software Engineering, Data & Analytics
Truata

Who we are
§ Truata was founded in 2018, with investment by
Mastercard and IBM
§ Our goal is to be the world’s leading provider of privacy-
enhanced data analytics and management solutions
§ Based in Dublin, we have a team of 70 people with an R&D
focus on developing cutting edge privacy enhancing
technologies (PETs)
§ International client base across major industry verticals
§ Multiple EU regulators consulted on the Truata solution
Truata Anonymization Service is a cutting-edge solution for GDPR-grade
data anonymization & analytics, allowing companies to analyse and monetize
customer data in fully-anonymized form.
§ Sophisticated and
proprietary
technologies for
data anonymization
and risk calibration
§ Able to generate
fully anonymized
data sets ideally
suited for privacy-
preserving analytics
§ Experienced in
delivering large-
scale anonymized
data analytics
projects
§ Able to drive
significant value
from data while
maintaining
customer trust
§ Delivered by our
customer success
team of data
science and privacy
experts
§ Fully focused on
using privacy-centric
techniques to
generate value from
data
§ Consulting solutions based on our proprietary
methodologies, IP and expertise delivered by
industry leading, subject matter experts.

A big-data privacy engineering problem
• Geo privacy
• Zip-level targeted advertising
• Lat/Long GPS
• Shapefile of zip codes
• Using neighbouring zip’s shopping behaviour
• Problems
• Lat/Long mapping (generalisation of GPS information) &
nearest 10
• (32m) customer’s Lat long mapping onto (1.7m) UK Zips
• (1.7m) nearest 10 Zips out of 1.3m Zips (with Customer
transactions)
• A ‘Trillion’ Problem
• 1,000,000,000,000
• Google processes 61.6 billion web pages today
• Dublin Population 2019: 1,214,666
• Measure the similarity of any two Dubliners

Definition of beauty
Definition of beauty
: the quality of being physically attractive.
: the qualities in a person or a thing that give
pleasure to the senses or the mind.
…
Beauty | Definition of Beauty by Merriam-Webster
https://www.merriam-webster.com › dictionary › beauty

Engineering (journey) is beautiful
A combination of 99% passion and 1% skills (big data engineering, spatial engineering and software engineering)

Summary
▪ Beauty of big data privacy engineering
▪ Geo anonymization and geospatial analytics
workloads with Spark
▪ Maximizing the performance by combining multi-
dimensional spatial indexing with Spark in-
memory computations
▪ Journey of productionizing the geo anonymization
workloads
▪ Craftsmanship spirit
▪ Ongoing work
▪ Mobility trajectory anonymization (patents pending)
▪ Mobility pattern anonymization
Contact
Email: yangcheng.huang_AT_truata.com
Linkedin: https://www.linkedin.com/in/yhuang
www.truata.com

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In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

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The Beauty of (Big) Data Privacy Engineering

1. The Beauty of (Big) Data Privacy Engineering Yangcheng Huang Director of Software Engineering, Data & Analytics Truata

2. Who we are § Truata was founded in 2018, with investment by Mastercard and IBM § Our goal is to be the world’s leading provider of privacy- enhanced data analytics and management solutions § Based in Dublin, we have a team of 70 people with an R&D focus on developing cutting edge privacy enhancing technologies (PETs) § International client base across major industry verticals § Multiple EU regulators consulted on the Truata solution Truata Anonymization Service is a cutting-edge solution for GDPR-grade data anonymization & analytics, allowing companies to analyse and monetize customer data in fully-anonymized form. § Sophisticated and proprietary technologies for data anonymization and risk calibration § Able to generate fully anonymized data sets ideally suited for privacy- preserving analytics § Experienced in delivering large- scale anonymized data analytics projects § Able to drive significant value from data while maintaining customer trust § Delivered by our customer success team of data science and privacy experts § Fully focused on using privacy-centric techniques to generate value from data § Consulting solutions based on our proprietary methodologies, IP and expertise delivered by industry leading, subject matter experts.

3. A big-data privacy engineering problem • Geo privacy • Zip-level targeted advertising • Lat/Long GPS • Shapefile of zip codes • Using neighbouring zip’s shopping behaviour • Problems • Lat/Long mapping (generalisation of GPS information) & nearest 10 • (32m) customer’s Lat long mapping onto (1.7m) UK Zips • (1.7m) nearest 10 Zips out of 1.3m Zips (with Customer transactions) • A ‘Trillion’ Problem • 1,000,000,000,000 • Google processes 61.6 billion web pages today • Dublin Population 2019: 1,214,666 • Measure the similarity of any two Dubliners

4. Definition of beauty Definition of beauty : the quality of being physically attractive. : the qualities in a person or a thing that give pleasure to the senses or the mind. … Beauty | Definition of Beauty by Merriam-Webster https://www.merriam-webster.com › dictionary › beauty

5. Processing result is beautiful

6. Engineering (algorithm) is beautiful

7. Engineering (journey) is beautiful A combination of 99% passion and 1% skills (big data engineering, spatial engineering and software engineering)

8. Craftsmanship spirit is beautiful

9. Summary ▪ Beauty of big data privacy engineering ▪ Geo anonymization and geospatial analytics workloads with Spark ▪ Maximizing the performance by combining multi- dimensional spatial indexing with Spark in- memory computations ▪ Journey of productionizing the geo anonymization workloads ▪ Craftsmanship spirit ▪ Ongoing work ▪ Mobility trajectory anonymization (patents pending) ▪ Mobility pattern anonymization Contact Email: yangcheng.huang_AT_truata.com Linkedin: https://www.linkedin.com/in/yhuang www.truata.com

10. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

The Beauty of (Big) Data Privacy Engineering

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