{"id":27843,"date":"2026-04-13T12:07:34","date_gmt":"2026-04-13T12:07:34","guid":{"rendered":"https:\/\/dataforpublicgood.org.in\/?p=27843"},"modified":"2026-04-13T12:16:06","modified_gmt":"2026-04-13T12:16:06","slug":"on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy","status":"publish","type":"post","link":"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy\/on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy\/","title":{"rendered":"On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering &#8211; Data Privacy"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"27843\" class=\"elementor elementor-27843\">\n\t\t\t\t\t\t<div class=\"elementor-inner\">\n\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2dac990 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2dac990\" 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-50 elementor-top-column elementor-element elementor-element-51804ec\" data-id=\"51804ec\" 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-5ac4424 elementor-widget elementor-widget-text-editor\" data-id=\"5ac4424\" 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\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-438085c elementor-widget elementor-widget-text-editor\" data-id=\"438085c\" 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<h1>On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering &#8211; Data Privacy<\/h1>\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-ff9263c elementor-widget elementor-widget-text-editor\" data-id=\"ff9263c\" 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 style=\"color: blue;\"><strong>Authors<\/strong>: Gokularam M, Anshoo 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>Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without additional privacy loss. The choice of the number of grids for discretizing the data points is crucial, as it directly controls the quantization bias and the amount of noise injected to preserve privacy. The widely adopted strategy selects a grid size that is independent of the number of clusters and also relies on empirical tuning. In this work, we revisit this choice and propose a refined grid-size selection rule derived by minimizing an upper bound on the expected deviation in the K-means objective function, leading to a more principled discretization strategy for non-interactive private clustering. Compared to prior work, our grid resolution differs both in its dependence on the number of clusters and in the scaling with dataset size and privacy budget. Extensive numerical results elucidate that the proposed strategy results in accurate clustering compared to the state-of-the-art techniques, even under tight privacy budgets.<\/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>SPCOM 2026\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\/1.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\/2603.26963%20\" 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>On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering &#8211; Data Privacy Authors: Gokularam M, Anshoo Tandon Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks &hellip;<\/p>\n","protected":false},"author":3,"featured_media":27845,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[92],"tags":[],"class_list":["post-27843","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 name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy\/on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Breaking Data Silos: How GDI is Transforming Access to Geospatial Information in India\" \/>\n<meta property=\"og: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 property=\"og:url\" content=\"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy\/on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy\/\" \/>\n<meta property=\"og:site_name\" content=\"Data for Public Good\" \/>\n<meta property=\"article:published_time\" content=\"2026-04-13T12:07:34+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-04-13T12:16:06+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dataforpublicgood.org.in\/cdpg\/wp-content\/uploads\/2026\/04\/1.jpg.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"878\" \/>\n\t<meta property=\"og:image:height\" content=\"772\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"akash markiverse\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AgriDataXchange\" \/>\n<meta name=\"twitter:site\" content=\"@AgriDataXchange\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"akash markiverse\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy\/on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy\/on-the-optimal-number-of-grids-for-differentially-private-non-interactive-k-means-clustering-data-privacy\/\"},\"author\":{\"name\":\"akash markiverse\",\"@id\":\"https:\/\/dataforpublicgood.org.in\/#\/schema\/person\/54d8c0d6f9b74f98a0484f2297837ec4\"},\"headline\":\"On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering &#8211; 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