<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">EIR</journal-id><journal-title-group><journal-title>Educational Innovation Research</journal-title></journal-title-group><issn>3029-1844</issn><eissn>3029-1852</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18063/EIR.v4i4.1955</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on a Universal Data Privacy Collaborative Protection Technology that Integrates Homomorphic Encryption and Secure Multi-party Computation</title><url>https://artdesignp.com/journal/EIR/4/4/10.18063/EIR.v4i4.1955</url><author>TianWenliang</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>4</issue><history><date date-type="pub"><published-time>2026-04-26</published-time></date></history><abstract>In big data interoperability scenarios, single privacy protection technologies struggle to balance the demands of efficient data processing with stringent privacy security requirements. Homomorphic encryption and secure multi-party computation, two core technologies in privacy computing, enable data to be &amp;ldquo;both usable and invisible.&amp;rdquo; However, these technologies are often applied independently or only superficially integrated, exhibiting common limitations such as high computational overhead, poor cross-platform interface compatibility, a lack of a unified protection framework, and limited general adaptability. This paper systematically analyzes the fundamental theories and operational characteristics of both technologies, identifies key challenges in their integration, and proposes a unified privacy protection architecture. The architecture optimizes hybrid algorithm operation modes and comprehensive permission management mechanisms while establishing lightweight, standardized, and dynamically adjustable optimization strategies. Key contributions include: developing a universal collaborative protection framework applicable to government, financial, and healthcare applications; overcoming the limitations of traditional single-technology approaches and superficial integration methods; achieving an effective balance between data privacy security and collaborative computing efficiency through optimized algorithms and standardized interfaces; filling research gaps in deeply integrated standardization frameworks for these technologies; and providing theoretical foundations and practical references for large-scale implementation of compliant cross-domain data sharing and privacy protection solutions.</abstract><keywords>Homomorphic encryption, Secure multi-party computation, Data privacy, Collaborative protection</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Kazuki I, Naoto Y, Paul JC, et al., 2022, SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning. Journal of Information Processing, 30: 209&amp;ndash;225.
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