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Reddy, N. Chakraborty, A. Dharmavaram, A. Tandon<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9514863 elementor-widget elementor-widget-text-editor\" data-id=\"9514863\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t<p>Data privacy and anonymisation are critical concerns in today&#8217;s data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available hardware resources provide an upper bound on the maximum size of dataset that can be processed at a time. Large datasets with sizes exceeding this upper bound must be broken up into smaller data chunks for processing. In these cases, standard k-anonymisation tools such as ARX can only operate on a per-chunk basis. This paper proposes SKALD, a novel algorithm for performing k-anonymisation on large datasets with limited RAM. Our SKALD algorithm offers multi-fold performance improvement over standard k-anonymisation methods by extracting and combining sufficient statistics from each chunk during processing to ensure successful k-anonymisation while providing better utility.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-85f0dee elementor-widget elementor-widget-text-editor\" data-id=\"85f0dee\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\">\n\t\t\t\t<strong>Journal\/Conference\n<\/strong> 2025 16th International IEEE Conference on Computing, Communication and Networking Technologies (ICCCNT)  \t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-979fd33\" data-id=\"979fd33\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4ce5c30 elementor-widget elementor-widget-image\" data-id=\"4ce5c30\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-image\">\n\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"878\" height=\"772\" src=\"https:\/\/dataforpublicgood.org.in\/cdpg\/wp-content\/uploads\/2026\/04\/4.jpg.jpeg\" class=\"attachment-full size-full\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c27cce3 elementor-button-success elementor-align-center elementor-widget elementor-widget-button\" data-id=\"c27cce3\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a href=\"https:\/\/arxiv.org\/abs\/2505.03529\" target=\"_blank\" class=\"elementor-button-link elementor-button elementor-size-sm elementor-animation-shrink\" role=\"button\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Know more<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d308ba elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d308ba\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e1458bc\" data-id=\"e1458bc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5229c9c elementor-widget elementor-widget-html\" data-id=\"5229c9c\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\n    .post-style-3 .entry-header {\n        display: none;\n    }\n    \n    h1 {\n        font-size:28px !important;\n        text-transform: inherit !important;\n    }\n    \n<\/style>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<div style=\"margin-top: 0px; margin-bottom: 0px;\" class=\"sharethis-inline-share-buttons\" ><\/div>","protected":false},"excerpt":{"rendered":"<p>SKALD: Scalable K-Anonymisation for Large Datasets\u00a0 Authors:K. Reddy, N. Chakraborty, A. Dharmavaram, A. Tandon Data privacy and anonymisation are critical concerns in today&#8217;s data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available hardware resources provide an upper bound on the maximum size of &hellip;<\/p>\n","protected":false},"author":3,"featured_media":27874,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[92],"tags":[],"class_list":["post-27873","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-privacy"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.12 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Breaking Data Silos: How GDI is Transforming Access to Geospatial Information in India<\/title>\n<meta name=\"description\" content=\"Designed to treat publicly funded geospatial data as a common good, GDI has established itself as a unified, open-access platform for interoperable, consent-based, analysis-ready, and metadata-rich data exchange using globally accepted standards.\" \/>\n<meta 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