Alibaba’s Secure Data Hub (SDH) leverages MPC, differential privacy and PPML to create a compliant data clean‑room for advertisers, allowing multi‑party data collaboration without exposing raw user data, thereby enhancing privacy, trust and efficiency in the advertising ecosystem.
This article presents a comprehensive exploration of using Apache Paimon and Flink to design lake tables that support minute‑level latency, low cost, and unified batch‑stream processing for advertising data, covering schema design, partitioning strategies, performance trade‑offs, cost analysis, and operational best practices.
This article analyzes the challenges of commercial real‑time data processing—such as stability, multi‑stage computation, and frequent schema changes—and explains how the RTS platform provides end‑to‑end managed solutions, auto schema handling, primary‑secondary redundancy, experiment‑first deployment, and metadata generation to unlock high‑velocity data value for advertising operations.
This article details how Alibaba Mama’s Secure Data Hub (SDH) leverages multi‑party computation, federated learning, and differential privacy to break data silos in advertising, enabling secure cross‑domain user tracking, full‑domain asset analysis, and rapid, privacy‑compliant marketing insights.
Alibaba Mama’s Secure Data Hub delivers a privacy‑enhanced clean‑room for advertising by combining multi‑party computation, federated learning and differential privacy with encrypted operators on a Flink engine, offering cloud‑agnostic, scalable deployment that enables cross‑domain analytics while protecting raw user data and boosting ROI.
The 2023 Big Data “Galaxy” case competition highlighted Alibaba Mama and Jiahe Tech’s privacy‑computing based cross‑domain advertising tracking and whole‑domain asset analysis as an outstanding example, detailing the Secure Data Hub platform, its use of multi‑party secure computation, federated learning, differential privacy, and the resulting improvements in data flow, compliance, and marketing efficiency.
This article shares a comprehensive guide on constructing commercial data products, covering advertising data use, the three-stage data infrastructure build, content modeling, and how data-driven platforms empower business decisions across executives, managers, and analysts.
This article summarizes Liu Chong's DTCC2022 talk on building a real‑time advertising data warehouse, covering business context, layered model design, development technologies such as Flink and Kafka, full‑link quality assurance, practical implementation details, and future architectural directions.
This article presents a comprehensive case study of building a real‑time advertising data warehouse at Auto Home, detailing the evaluation of streaming engines and storage solutions, the layered architecture design, implementation steps with Flink and StarRocks, monitoring practices, encountered issues, and future roadmap, demonstrating how second‑level data freshness and high accuracy were achieved.
In a July 8 live broadcast, Tencent Advertising’s strategy algorithm team explained experimental design for two‑sided markets, covering control‑variable selection, CUPED variance reduction, Bayesian smoothing, and bias metrics, and answered participant questions with practical examples and guidance.
This article walks through the complete implementation of an advertising statistics pipeline using Spark and Kudu, covering requirement analysis, Scala code development, SQL queries, schema definition, and data sinking, with full code snippets and execution results.
This article explains how marketers can use UTM parameters to identify the source of ad clicks, compares click‑based (CPC) and impression‑based (CPM) pricing models, and introduces a cryptographic private‑set‑intersection protocol that lets advertisers and e‑commerce platforms measure ad exposure effects while fully protecting user privacy.
This article details the design and implementation of ADMonitor, a high‑availability, low‑latency advertising monitoring platform built on OpenResty, covering its architecture, data collection, processing, archiving, and performance outcomes.
