[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85058-en":3,"doc-seo-85058-105":29,"detail-sidebar-cat-0-en-105":90},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85058,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","MLQENABLER Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing","Cloud computing enables public cloud service providers to offer machine learning services by using clients’ stored data, creating a CSS-MLS model with new business opportunities. Security concerns arise because public clouds cannot be fully trusted, including risks of sensitive-data resale, insider access, and external hacking. Encrypting outsourced databases protects privacy but destroys data utility, since encrypted databases reveal only pseudorandom values. MLQENABLER proposes an index-aid scheme enabling secure ML queries over encrypted cloud databases, delivering acceptable security with only slight ML performance degradation.","MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted  \nDatabase in Cloud Computing  \nXu Zhou, Haoyang Chen, Xinyu Lei  \nDepartment of Computer Science, Michigan Technological University, Houghton, MI, USA E-mail: {xzhou4, haoyangc, [xinyulei](xinyulei}@mtu.edu)[}](xinyulei}@mtu.edu)[@mtu.edu](xinyulei}@mtu.edu)  \narXiv :2607 .08 197v 1 [ cs .CR] 9 Jul 2026  \nAbstract—In cloud computing, the public cloud service providers (CSPs) can provide cloud storage as the primary service while providing additional machine learning (ML)-based services by using the clients’ data in storage. This business model extends the border of cloud computing services and brings in new business growth possibilities. Although it is promising, the model also brings in security concerns since the public commercial cloud cannot be fully trusted. For example, the public commercial clouds may sell clients’ sensitive data to the government or other companies. To address the security concerns, an immediate solution is to require clients to encrypt their datasets before outsourcing to the cloud. However, if a database is formally encrypted, then the database contains only pseudorandom numbers, making it impossible to enable ML over it. In this project, we propose MLQENABLER (ML Queries Enabler) scheme to enable secure ML queries over encrypted database in cloud storage. MLQENABLER employs an index-aid approach to achieve security and ML capability simultaneously. Our initial experiments show that MLQENABLER achieves an acceptable security level while incurring only a slight ML performance degradation.  \nI. INTRODUCTION Background and Motivation. In many real-world business models, clients’ data is accessible by their service providers.  \nMultiple clients can enjoy the services provided by the centralized service providers. Meanwhile, the service providers can exploit the merged data from multiple clients to deliver some extra machine learning (ML)-based services. For example, when clients use Amazon to search and purchase products, Amazon can collect clients’ historical search/purchase data to build its ML-based recommendation system. We call the above business model as Primary Service with Machine Learningbased Service (PS-MLS) model. In the above example, the primary service is the online shipping provided by Amazon, the ML-based service is the product recommendation. PS-MLS model is quite common in practice as evidenced by the fact that most newly installed apps would pop up a window to ask for the permission to access some users’ private data. Once permitted, the app-collected data can be used to provide MLbased services.  \nAs an instance of PS-MLS model, in cloud computing, the cloud storage service (CSS) can be treated as the primary service while the cloud service provider (CSP) can also offer extra ML-based services by leveraging the massive datasets stored in the cloud. For brevity, this business model is named CSS-MLS model in this paper. Compared with local data storage, CSS has lower costs, better performance, and higher flexibility. As a result, CSS is increasingly prevalent and the  \ncloud-host data volumes grow exponentially. According to ComputerWeekly [1], the volume of data on earth will increase to 175 ZB (1 ZB=1012 GB) by 2025 and half of data on earth will be stored in public clouds. Due to the huge volume, even a small portions of cloud-host data is sufficient to train good ML models. Therefore, enabling ML services in CSS is a natural and pressing demand. CSS-MLS model captures the spirit of the data sharing economy in the big data era and is expected to continue to expand in the future.  \nPromising as it is, CSS-MLS model also raises security threats towards clients’ outsourced data because the public cloud cannot be fully trusted. First, there are financial incentives for CSPs to sell clients’ personal data to the government or other companies. In addition, some corrupted cloud employees with administrator privileges may ","cbCaidKNgYNltSo9","https://ap.wps.com/l/cbCaidKNgYNltSo9","pdf",2314653,3,1,"English","en",105,"# Introduction\n## Background and Motivation\n## Security Threats and Need for Encrypted ML\n## Limitations of Prior Art","[{\"question\":\"What security problem does the CSS-MLS model create in cloud computing?\",\"answer\":\"The public cloud cannot be fully trusted, so clients’ outsourced data may be exposed to resale incentives, insider administrators accessing data, or external hacking attacks.\"},{\"question\":\"Why is enabling ML over an encrypted database difficult?\",\"answer\":\"Formally encrypted databases contain only pseudorandom numbers, preventing the system from using the data to perform normal ML operations and thus harming data utility.\"},{\"question\":\"How does MLQENABLER aim to provide secure ML queries while preserving usability?\",\"answer\":\"MLQENABLER introduces an index-aid approach that enables both security and ML capability simultaneously, with initial experiments showing acceptable security and only slight ML performance 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