[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121916-en":3,"doc-seo-121916-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121916,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",6,"Technology","GuardML - GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption","Machine learning enables powerful predictions but also expands privacy risks through malicious attacks on models and training data. Privacy-Preserving Machine Learning (PPML) addresses this by enabling secure learning while keeping inputs and models confidential. Traditional homomorphic encryption (HE) is often too inefficient and impractical at scale. This work introduces Hybrid Homomorphic Encryption (HHE) for end devices, using HHE to securely compute classification outcomes over encrypted data with preserved privacy. Results show real-world applicability via an HHE-based PPML system for heart-disease ECG classification, with only a slight accuracy reduction and minimal communication and computation overhead for both analysts and end devices.","GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption  \nEugene Frimpong  \nTampere University Tampere, Finland [eugene.frimpong@tuni.fi](eugene.frimpong@tuni.fi)  \nKhoa Nguyen Tampere University  \nTampere, Finland [khoa.nguyen@tuni.fi](khoa.nguyen@tuni.fi)  \nMindaugas Budzys  \nTampere University Tampere, Finland [mindaugas.budzys@tuni.fi](mindaugas.budzys@tuni.fi)  \narXiv :2401 . 14840v1 [ cs .LG] 26 Jan 2024  \nTanveer Khan Tampere University  \nTampere, Finland [tanveer.khan@tuni.fi](tanveer.khan@tuni.fi)  \nAntonis Michalas  \nTampere University Tampere, Finland [antonios.michalas@tuni.fi](antonios.michalas@tuni.fi)  \nABSTRACT  \nMachine Learning (ML) has emerged as one of data science’s most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns owing to the increasing number of malicious attacks targeting ML models. To address these concerns, Privacy-Preserving Machine Learning (PPML) methods have been introduced to safeguard the privacy and security of ML models. One such approach is the use of Homomorphic Encryption (HE) . However, the significant drawbacks and inefficiencies of traditional HE render it impractical for highly scalable scenarios. Fortunately, a modern cryptographic scheme, Hybrid Homomorphic Encryption (HHE), has recently emerged, combining the strengths of symmetric cryptography and HE to surmount these challenges. Our work seeks to introduce HHE to ML by designing a PPML scheme tailored for end devices. We leverage HHE as the fundamental building block to enable secure learning of classification outcomes over encrypted data, all while preserving the privacy of the input data and ML model. We demonstrate the real-world applicability of our construction by developing and evaluating an HHE-based PPML application for classifying heart disease based on sensitive ECG data. Notably, our evaluations revealed a slight reduction in accuracy compared to inference on plaintext data. Additionally, both the analyst and end devices experience minimal communication and computation costs, underscoring the practical viability of our approach. The successful integration of HHE into PPML provides a glimpse into a more secure and privacy-conscious future for machine learning on relatively constrained end devices.  \nKEYWORDS  \nHybrid Homomorphic Encryption, Machine Learning as a Service, Privacy-Preserving Machine Learning  \n1 INTRODUCTION  \nMachine Learning (ML) has increasingly become one of the most impactful fields of data science in recent years, allowing various users to classify and make predictions based on multi-dimensional data. One of the main metrics for ML is the accuracy of the prediction or classification results. However, to achieve this, the results should be accompanied by a large amount of high-quality training data requiring the collaboration of several organizations. Currently, regulations such as the General Data Protection Regulations (GDPR)  \nforbid the sharing and processing of sensitive data without the data subject’s consent. It has, therefore, become crucial to uphold data privacy and confidentiality when obtaining data from other organizations. One solution to this problem is employing PrivacyPreserving Machine Learning (PPML). PPML ensures that the use of data protects user privacy and that data is utilized in a safe fashion, avoiding leakage of confidential and private information. To this end, researchers have proposed and implemented various PPMLachieving techniques, varying from secure cryptographic schemes to distributed, hybrid, and data modification approaches. This work focuses on cryptographic approaches, the most commonly used one being Homomorphic Encryption (HE) [14, 28], which exhibits a high potential in ML applications.  \nHE allows users to perform computations such as addition or multiplication on encrypted data [28] . One of the first fully HE (FHE) schemes was proposed by C. G","cbCaipwrAvbCpVnC","https://ap.wps.com/l/cbCaipwrAvbCpVnC","pdf",906325,1,10,"English","en",105,"# Abstract\n# Introduction\n## Privacy concerns in machine learning adoption\n## Homomorphic encryption and its limitations\n## Hybrid homomorphic encryption concept","[{\"question\":\"Why does machine learning adoption create privacy concerns?\",\"answer\":\"Widespread ML use increases exposure to malicious attacks targeting ML models and the sensitive data used for training and inference.\"},{\"question\":\"What makes traditional homomorphic encryption impractical in large-scale PPML scenarios?\",\"answer\":\"Traditional HE often suffers from high computational complexity and extended ciphertext expansion, leading to very large ciphertexts and overhead.\"},{\"question\":\"How does the proposed GuardML approach use hybrid homomorphic encryption?\",\"answer\":\"GuardML encrypts data locally with symmetric encryption, homomorphically encrypts the symmetric key with HE, and lets the server convert it into a homomorphic ciphertext to perform secure classification over encrypted data.\"}]","GuardML - GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption | PDF",1785807725,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"guardml-guardml-efficient-privacy-preserving-machine-learning-services-through-hybrid-homomorphic-encryption","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/guardml-guardml-efficient-privacy-preserving-machine-learning-services-through-hybrid-homomorphic-encryption/121916/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does machine learning adoption create privacy concerns?","Question",{"text":75,"@type":76},"Widespread ML use increases exposure to malicious attacks targeting ML models and the sensitive data used for training and inference.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes traditional homomorphic encryption impractical in large-scale PPML scenarios?",{"text":80,"@type":76},"Traditional HE often suffers from high computational complexity and extended ciphertext expansion, leading to very large ciphertexts and overhead.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed GuardML approach use hybrid homomorphic encryption?",{"text":84,"@type":76},"GuardML encrypts data locally with symmetric encryption, homomorphically encrypts the symmetric key with HE, and lets the server convert it into a homomorphic ciphertext to perform secure classification over encrypted data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]