Horizontal Data Partitioning

Horizontal Data Partitioning refers to the process of splitting a dataset by rows, where each partition contains the same set of features but different records. This concept is fundamental to Federated Learning, as it allows multiple entities to train a shared model without ever exchanging raw data. This aligns with the GDPR principle of data minimization (Article 5) and the Taiwan Personal Data Protection Act's restrictions on data-sharing. Unlike vertical partitioning, which splits data by columns, horizontal partitioning is used when different entities have the same type of information about different subjects. This technique is critical for AI governance, as it mitigates the risk of centralized data breaches by keeping the raw data at its source, thus reducing the attack surface for data-at-rest and data-in-transit vulnerabilities.

Implementation typically follows three steps: 1. Data-centric risk assessment to identify sensitive features; 2. Deployment of a privacy-preserving framework (e.g., Federated Learning); 3. Continuous monitoring of model-based privacy attacks. For instance, a group of banks in Taiwan could be trained on a collective money-laundering detection model without sharing customer identities. This approach has demonstrated a 35% reduction in data-related compliance risks in similar EU-based implementations. Key performance indicators (KPIs) include the reduction in data-sharing-related legal incidents (target: zero), model-to-data-leakage-risk-ratio (target: <0.01%), and compliance-related-cost-saving (estimated at 20% annually).

Taiwan enterprises face three primary challenges: First, the legal interpretation of 'anonymization' under the Taiwan Personal Data Protection Act, which may be stricter than the GDPR's 'anonymization' standard. Second, the technical complexity of ensuring model-based privacy-preserving-guarantees (e.g., DP-SGD). Third, the lack of inter-organizational trust-building frameworks. To overcome these, enterprises should: 1. Implement Differential Privacy (DP) to provide mathematical guarantees of anonymity; 2. Establish a Data-Sharing-as-a-Service (DSaaS) governance model; 3. Partner with specialized consultants like Winners Consulting to ensure the implementation meets both technical and legal standards within the first 6 months.

Winners Consulting Services Co., Ltd.專注臺灣企業Horizontal Data Partitioning相關議題，擁有豐富實戰輔導經驗，協助企業在90天內建立符合國際標準的管理機制，已服務超過100家臺灣企業。申請免費機制診斷：https://winners.com.tw/contact