{"id":776,"date":"2026-03-17T22:42:54","date_gmt":"2026-03-17T21:42:54","guid":{"rendered":"https:\/\/coneixement.info\/blog\/?p=776"},"modified":"2026-03-17T22:48:48","modified_gmt":"2026-03-17T21:48:48","slug":"arx-anonymization-tool-guia-practica-per-anonimitzar-dades-de-recerca-2","status":"publish","type":"post","link":"https:\/\/coneixement.info\/blog\/arx-anonymization-tool-guia-practica-per-anonimitzar-dades-de-recerca-2\/","title":{"rendered":"ARX Anonymization Tool: guia pr\u00e0ctica per anonimitzar dades de recerca"},"content":{"rendered":"<!--\r\n===========================================\r\n  METADADES SEO \u2014 Copia aquestes dades\r\n  al camp corresponent del teu WordPress\r\n===========================================\r\n\r\nT\u00cdTOL SEO:\r\nARX Anonymization Tool: guia pr\u00e0ctica per anonimitzar dades cl\u00edniques\r\n\r\nMETA DESCRIPCI\u00d3 (\u2264155 car\u00e0cters):\r\nApr\u00e8n a usar ARX per anonimitzar dades de recerca pas a pas. k-anonimitat, l-diversity i t-closeness explicats amb exemples reals.\r\n\r\nSLUG URL SUGGERIT:\r\narx-anonymization-tool-guia-practica\r\n\r\nETIQUETES SUGGERIDES:\r\nprivacitat de dades, anonimitzaci\u00f3, ARX, recerca cl\u00ednica, RGPD, doctorat, k-anonimitat, ci\u00e8ncia de dades, salut digital\r\n\r\nCATEGORIES SUGGERIDES:\r\nRecerca \u00b7 Metodologia \u00b7 Privacitat de dades\r\n\r\nIMATGE DESTACADA SUGGERIDA:\r\nUna visualitzaci\u00f3 d'una taula de dades amb columnes parcialment emmascarades,\r\no el logo d'ARX sobre un fons de dades cl\u00edniques abstractes.\r\n\r\nTEMPS DE LECTURA ESTIMAT: 14\u201316 minuts\r\n===========================================\r\n-->\r\n<p><style>\r\n\/* \u2500\u2500 Reset m\u00ednim per a WordPress \u2500\u2500 *\/\r\n.arx-article * { box-sizing: border-box; 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}\r\n.arx-cta-btn.outline { background: transparent; color: #fff; border: 2px solid rgba(255,255,255,0.7); }\r\n\r\n\/* \u2500\u2500 TOC \u2500\u2500 *\/\r\n.arx-toc { background: #f5f9fc; border: 1px solid #cde3f5; border-radius: 8px; padding: 1.25rem 1.5rem; margin: 2rem 0; }\r\n.arx-toc p { font-weight: 700; color: #1F5C8B; margin: 0 0 0.6rem; }\r\n.arx-toc ol { margin: 0; padding-left: 1.3rem; }\r\n.arx-toc li { margin-bottom: 0.3rem; font-size: 0.97rem; }\r\n.arx-toc a { color: #2E86AB; text-decoration: none; }\r\n.arx-toc a:hover { text-decoration: underline; }\r\n\r\n\/* \u2500\u2500 Responsive \u2500\u2500 *\/\r\n@media (max-width: 640px) {\r\n  .arx-hero h1 { font-size: 1.6rem; }\r\n  .arx-hero { padding: 2rem 1.5rem; }\r\n  .arx-article h2 { font-size: 1.4rem; }\r\n}\r\n<\/style><\/p>\r\n<article class=\"arx-article\"><!-- \u2500\u2500 HERO \u2500\u2500 -->\r\n<div class=\"arx-hero\">\r\n<p>Tot el que necessites saber sobre k-anonimitat, l-diversity i t-closeness, amb exemples reals pas a pas i consells per comen\u00e7ar a fer servir l&#8217;eina d&#8217;anonimitzaci\u00f3 ARX.<\/p>\r\n<div class=\"arx-meta\">Recerca &amp; MetodologiaTemps de lectura: ~15 minNivell: IntermediEina: ARX v3.9+<\/div>\r\n<\/div>\r\n<!-- \u2500\u2500 INTRO \u2500\u2500 -->\r\n<div class=\"arx-intro-box\">\r\n<p>Si treballes amb dades de pacients, enquestes o qualsevol dataset que contingui informaci\u00f3 personal, <strong>anonimitzar correctament \u00e9s una obligaci\u00f3 legal i \u00e8tica<\/strong>. ARX \u00e9s l&#8217;eina de refer\u00e8ncia en recerca cl\u00ednica i biom\u00e8dica, gratu\u00efta i de codi obert. Aquesta guia t&#8217;explica com funciona i com usar-la des de zero.<\/p>\r\n<\/div>\r\n<!-- \u2500\u2500 TAULA DE CONTINGUTS \u2500\u2500 --><nav class=\"arx-toc\">\r\n<p>Continguts d&#8217;aquest article<\/p>\r\n<ol>\r\n<li><a href=\"#que-es-arx\">Qu\u00e8 \u00e9s ARX i per a qui \u00e9s \u00fatil<\/a><\/li>\r\n<li><a href=\"#tipus-atributs\">Pas 1: Classificar els atributs del dataset<\/a><\/li>\r\n<li><a href=\"#models-atac\">Pas 2: Entendre els models d&#8217;atac<\/a><\/li>\r\n<li><a href=\"#k-anonimitat\">Pas 3: k-Anonimitat \u2014 el fonament<\/a><\/li>\r\n<li><a href=\"#l-diversity\">Pas 4: l-Diversity \u2014 protegir el diagn\u00f2stic<\/a><\/li>\r\n<li><a href=\"#t-closeness\">Pas 5: t-Closeness \u2014 l&#8217;\u00faltim escut<\/a><\/li>\r\n<li><a href=\"#gui-arx\">Pas 6: Usar la GUI d&#8217;ARX pas a pas<\/a><\/li>\r\n<li><a href=\"#resum\">Resum i checklist final<\/a><\/li>\r\n<\/ol>\r\n<\/nav><!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 -->\r\n<h2 id=\"que-es-arx\">Qu\u00e8 \u00e9s ARX i per a qui \u00e9s \u00fatil?<\/h2>\r\n<p>ARX (ARX Data Anonymization Tool) \u00e9s una eina de codi obert desenvolupada per Florian Prasser i col\u00b7laboradors, dissenyada espec\u00edficament per a la <strong>anonimitzaci\u00f3 de dades tabulars<\/strong>. T\u00e9 interf\u00edcie gr\u00e0fica (GUI) i API per a Java, cosa que la fa accessible tant per a investigadors sense coneixements de programaci\u00f3 com per a equips de data science que volen automatitzar el proc\u00e9s.<\/p>\r\n<p>\u00c9s especialment popular en:<\/p>\r\n<ul>\r\n<li><strong>Recerca cl\u00ednica i biom\u00e8dica<\/strong>: datasets de pacients, histories cl\u00edniques, estudis epidemiol\u00f2gics<\/li>\r\n<li><strong>Ci\u00e8ncies socials<\/strong>: enquestes, dades socioecon\u00f2miques, censos<\/li>\r\n<li><strong>Tesis doctorals<\/strong> que treballen amb dades personals i han de complir amb el RGPD<\/li>\r\n<li><strong>Publicaci\u00f3 de datasets oberts<\/strong> en repositoris institucionals o Zenodo<\/li>\r\n<\/ul>\r\n<div class=\"arx-callout callout-blue\"><span class=\"arx-callout-label\">Per on comen\u00e7ar<\/span>\r\n<p>Descarrega ARX gratu\u00eftament a <strong>arx.deidentifier.org\/downloads<\/strong>. Requereix Java 11 o superior. A la mateixa p\u00e0gina trobar\u00e0s un projecte d&#8217;exemple que pots obrir directament per explorar la interf\u00edcie.<\/p>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"tipus-atributs\">Pas 1: Classificar els atributs del dataset<\/h2>\r\n<p>Abans de tocar cap par\u00e0metre, cal entendre que no totes les columnes del teu dataset creen el mateix risc. ARX distingeix cinc tipus d&#8217;atributs:<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>Tipus a ARX<\/th>\r\n<th>Risc<\/th>\r\n<th>Acci\u00f3 autom\u00e0tica<\/th>\r\n<th>Exemple t\u00edpic<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td><strong>Identifying<\/strong><\/td>\r\n<td>Molt alt<\/td>\r\n<td>Elimina la columna del dataset de sortida<\/td>\r\n<td>DNI, n\u00famero de SS, nom complet<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Quasi-identifying (QI)<\/strong><\/td>\r\n<td>Mig (perill\u00f3s en combinaci\u00f3)<\/td>\r\n<td>Generalitza o suprimeix<\/td>\r\n<td>Edat, codi postal, sexe<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Sensitive<\/strong><\/td>\r\n<td>Alt per infer\u00e8ncia<\/td>\r\n<td>Protegit pel model de privacitat triat<\/td>\r\n<td>Diagn\u00f2stic, salari, addicci\u00f3<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Insensitive<\/strong><\/td>\r\n<td>Negligible<\/td>\r\n<td>Es mant\u00e9 sense canvis<\/td>\r\n<td>Medicaci\u00f3 gen\u00e8rica, grup sanguini<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Response variable<\/strong><\/td>\r\n<td>Context-dependent<\/td>\r\n<td>Tractat com insensible per defecte<\/td>\r\n<td>Variable de resultat cl\u00ednic<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<h3>Per qu\u00e8 els quasi-identificadors son tan perillosos?<\/h3>\r\n<p>Latanya Sweeney va demostrar l&#8217;any 2000 que combinant <strong>data de naixement + sexe + codi postal<\/strong>, es pot identificar el 87% de la poblaci\u00f3 dels EUA. A Espanya, la situaci\u00f3 \u00e9s similar: el codi postal de 5 d\u00edgits combinat amb edat i sexe pot ser suficient per identificar individus en zones poc poblades.<\/p>\r\n<div class=\"arx-callout callout-red\"><span class=\"arx-callout-label\">Exemple real: l&#8217;atac de combinaci\u00f3<\/span>\r\n<p>Imagina que publiques una llista d&#8217;altes hospital\u00e0ries amb edat, codi postal i sexe (sense nom ni DNI). Un atacant pot creuar aquesta llista amb el cens electoral del mateix districte i identificar la majoria de pacients. Eliminar els identificadors directes <em>no \u00e9s suficient<\/em>.<\/p>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"models-atac\">Pas 2: Entendre els models d&#8217;atac<\/h2>\r\n<p>ARX no aplica una protecci\u00f3 gen\u00e8rica: et demana que decideixis <strong>contra quin perfil d&#8217;atacant vols protegir-te<\/strong>. S\u00f3n tres models ben diferenciats:<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>Model<\/th>\r\n<th>L&#8217;atacant sap que&#8230;<\/th>\r\n<th>Quan usar-lo<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td><strong>Prosecutor<\/strong><\/td>\r\n<td>Un individu concret \u00e9s al dataset i vol confirmar-ho<\/td>\r\n<td>Dades molt sensibles, obligaci\u00f3 legal de protegir individus espec\u00edfics<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Journalist<\/strong><\/td>\r\n<td>Hi ha alg\u00fa al dataset que compleix un perfil i vol trobar qui \u00e9s<\/td>\r\n<td>Publicaci\u00f3 de dades obertes o en repositoris acad\u00e8mics<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Marketer<\/strong><\/td>\r\n<td>El dataset existeix i vol re-identificar el m\u00e0xim d&#8217;individus<\/td>\r\n<td>Publicaci\u00f3 p\u00fablica massiva, datasets per a ML<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<p>Per a recerca doctoral amb dades cl\u00edniques, el m\u00ednim recomanable \u00e9s <strong>el model Journalist<\/strong>. Si el dataset cont\u00e9 dades especialment sensibles (salut mental, VIH, addiccions), considera el model Prosecutor.<\/p>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"k-anonimitat\">Pas 3: k-Anonimitat \u2014 el fonament<\/h2>\r\n<p>La <strong>k-anonimitat<\/strong> \u00e9s el model de privacitat base que gaireb\u00e9 sempre aplicar\u00e0s. La idea \u00e9s senzilla: cap individu ha de poder distingir-se de <em>com a m\u00ednim k-1 altres persones<\/em> en el dataset.<\/p>\r\n<p>Per aconseguir-ho, ARX <strong>generalitza<\/strong> els quasi-identificadors (substitueix valors exactes per rangs o categories m\u00e9s amples) fins que cada combinaci\u00f3 de QI apareix almenys k vegades. A aquests grups se&#8217;ls anomena <strong>classes d&#8217;equival\u00e8ncia<\/strong>.<\/p>\r\n<h3>Exemple pr\u00e0ctic: de valors exactes a classes d&#8217;equival\u00e8ncia<\/h3>\r\n<p>Tenim 6 pacients. Columnes QI: Edat, Codi Postal, Sexe. Atribut sensible: Diagn\u00f2stic.<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>ID<\/th>\r\n<th>Edat orig.<\/th>\r\n<th>Edat (k=2)<\/th>\r\n<th>Codi Postal orig.<\/th>\r\n<th>CP (k=2)<\/th>\r\n<th>Sexe<\/th>\r\n<th>Diagn\u00f2stic<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>P001<\/td>\r\n<td>29<\/td>\r\n<td>20\u201330<\/td>\r\n<td>08001<\/td>\r\n<td>080**<\/td>\r\n<td>F<\/td>\r\n<td>Ins. card\u00edaca<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>P002<\/td>\r\n<td>31<\/td>\r\n<td>20\u201330 \u2192 30\u201340<\/td>\r\n<td>08001<\/td>\r\n<td>080**<\/td>\r\n<td>F<\/td>\r\n<td>Hipertensi\u00f3<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>P003<\/td>\r\n<td>45<\/td>\r\n<td>40\u201350<\/td>\r\n<td>08010<\/td>\r\n<td>080**<\/td>\r\n<td>M<\/td>\r\n<td>Fibril. auricular<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>P004<\/td>\r\n<td>47<\/td>\r\n<td>40\u201350<\/td>\r\n<td>08010<\/td>\r\n<td>080**<\/td>\r\n<td>M<\/td>\r\n<td>Fibril. auricular<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>P005<\/td>\r\n<td>52<\/td>\r\n<td>50\u201360<\/td>\r\n<td>08015<\/td>\r\n<td>080**<\/td>\r\n<td>F<\/td>\r\n<td>Cardiopatia isq.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>P006<\/td>\r\n<td>54<\/td>\r\n<td>50\u201360<\/td>\r\n<td>08015<\/td>\r\n<td>080**<\/td>\r\n<td>F<\/td>\r\n<td>Hipertensi\u00f3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<p>Ara P001 i P002 formen una classe (si generalitzem prou l&#8217;edat), P003 i P004 en formen una altra, i P005 i P006 una tercera. Amb k=2 cap individu es pot distingir de l&#8217;altre dins de la seva classe.<\/p>\r\n<h3>Quant val de k has de triar?<\/h3>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>k<\/th>\r\n<th>Protecci\u00f3<\/th>\r\n<th>P\u00e8rdua d&#8217;info<\/th>\r\n<th>Recomanat per a&#8230;<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td><strong>k = 2<\/strong><\/td>\r\n<td><span class=\"badge-warn\">M\u00ednima<\/span><\/td>\r\n<td>Baixa<\/td>\r\n<td>\u00das intern en consorcis tancats<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>k = 3\u20135<\/strong><\/td>\r\n<td><span class=\"badge-ok\">Bona<\/span><\/td>\r\n<td>Moderada<\/td>\r\n<td>Publicaci\u00f3 acad\u00e8mica est\u00e0ndard<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>k \u2265 10<\/strong><\/td>\r\n<td><span class=\"badge-ok\">Alta<\/span><\/td>\r\n<td>Alta<\/td>\r\n<td>Requisits HIPAA, dades molt sensibles<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<div class=\"arx-callout callout-amber\"><span class=\"arx-callout-label\">Atenci\u00f3: la trampa de k-anonimitat<\/span>\r\n<p>Si tots els membres d&#8217;una classe d&#8217;equival\u00e8ncia tenen el <em>mateix diagn\u00f2stic<\/em>, un atacant pot inferir-lo sense necessitat d&#8217;identificar ning\u00fa. Exemple: si tots els homes de 40\u201350 anys de Barcelona del dataset han estat hospitalitzats per fibril\u00b7laci\u00f3 auricular, saber que alg\u00fa pertany a aquest grup ja revela el diagn\u00f2stic. Aqu\u00ed entra <strong>l-diversity<\/strong>.<\/p>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"l-diversity\">Pas 4: l-Diversity \u2014 protegir el diagn\u00f2stic<\/h2>\r\n<p>La <strong>l-diversity<\/strong> afegeix un requisit sobre l&#8217;atribut sensible: dins de cada classe d&#8217;equival\u00e8ncia, hi ha d&#8217;haver almenys <em>l valors ben representats<\/em> de l&#8217;atribut sensible. Aix\u00f2 evita que un atacant pugui inferir el diagn\u00f2stic, addicci\u00f3 o qualsevol altra dada sensible fins i tot sense saber qui \u00e9s l&#8217;individu.<\/p>\r\n<h3>Les tres variants principals<\/h3>\r\n<ul>\r\n<li><strong>Distinct l-diversity<\/strong> \u2014 la m\u00e9s simple: almenys <em>l<\/em> valors distints per classe. Suficient quan tots els valors de l&#8217;atribut sensible son igualment sensibles.<\/li>\r\n<li><strong>Entropy l-diversity<\/strong> \u2014 la m\u00e9s robusta: l&#8217;entropia de Shannon de la distribuci\u00f3 de l&#8217;atribut sensible ha de ser \u2265 log(<em>l<\/em>). Detecta casos on un valor domina fins i tot si n&#8217;hi ha <em>l<\/em> de distints.<\/li>\r\n<li><strong>Recursive (c, l)-diversity<\/strong> \u2014 interm\u00e8dia: el valor m\u00e9s freq\u00fcent no pot concentrar massa quota relativa respecte als altres.<\/li>\r\n<\/ul>\r\n<h3>C\u00e0lcul d&#8217;entropy l-diversity: exemple pas a pas<\/h3>\r\n<p>Tenim una classe amb 4 pacients i diagn\u00f2stics: Fibril\u00b7laci\u00f3 auricular (\u00d72), Cardiopatia isqu\u00e8mica (\u00d71), Hipertensi\u00f3 (\u00d71). Comprovem si satisf\u00e0 <strong>entropy 2-diversity<\/strong>:<\/p>\r\n<div class=\"arx-code-wrap\"><span class=\"arx-code-label\">C\u00e0lcul<\/span>\r\n<div class=\"arx-code\"><code><span class=\"cm\"># Distribuci\u00f3 de l'atribut sensible a la classe<\/span>\r\nFibril. auricular:    2\/4 = <span class=\"val\">0.50<\/span>\r\nCardiopatia isq.:     1\/4 = <span class=\"val\">0.25<\/span>\r\nHipertensi\u00f3:          1\/4 = <span class=\"val\">0.25<\/span>\r\n\r\n<span class=\"cm\"># Entropy de Shannon<\/span>\r\nH = -(0.50 \u00d7 log\u2082(0.50)) - (0.25 \u00d7 log\u2082(0.25)) - (0.25 \u00d7 log\u2082(0.25))\r\nH = 0.50 + 0.50 + 0.50 = <span class=\"val\">1.50 bits<\/span>\r\n\r\n<span class=\"cm\"># Requisit per a Entropy 2-diversity: H \u2265 log\u2082(2) = 1.0<\/span>\r\n1.50 \u2265 1.0  \u2192  <span class=\"ok\">SATISFET \u2713<\/span><\/code><\/div>\r\n<\/div>\r\n<div class=\"arx-callout callout-green\"><span class=\"arx-callout-label\">Recomanaci\u00f3 per a dades cl\u00edniques<\/span>\r\n<p>Usa <strong>Entropy l-diversity amb l = 3<\/strong> per a datasets cl\u00ednics on hi ha diagn\u00f2stics molt prevalents (com la hipertensi\u00f3 o la diabetis). La variant Distinct pot ser insuficient si un diagn\u00f2stic concentra el 70\u201380% dels registres d&#8217;una classe.<\/p>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"t-closeness\">Pas 5: t-Closeness \u2014 l&#8217;\u00faltim escut<\/h2>\r\n<p>Fins i tot amb l-diversity, pot passar que una classe d&#8217;equival\u00e8ncia tingui una distribuci\u00f3 de diagn\u00f2stics molt diferent de la distribuci\u00f3 global del dataset. Si un diagn\u00f2stic molt rar a la poblaci\u00f3 general \u00e9s molt freq\u00fcent en una classe concreta, un atacant que sap que alg\u00fa pertany a aquella classe pot inferir el diagn\u00f2stic amb alta probabilitat.<\/p>\r\n<p><strong>t-Closeness<\/strong> exigeix que la distribuci\u00f3 de l&#8217;atribut sensible dins de cada classe no difereixi en m\u00e9s de <em>t<\/em> de la distribuci\u00f3 global. La dist\u00e0ncia s&#8217;avalua amb <em>Earth Mover&#8217;s Distance (EMD)<\/em>.<\/p>\r\n<h3>Intu\u00efci\u00f3 visual<\/h3>\r\n<p>Pensa-ho com un embut d&#8217;arena: la distribuci\u00f3 global \u00e9s la forma que t\u00e9 la platja (30% IC, 20% FA, 20% HTA, 30% Cardiopatia). Cada classe \u00e9s un got ple d&#8217;arena. t-Closeness exigeix que la forma de l&#8217;arena al got s&#8217;assembli prou a la de la platja.<\/p>\r\n<div class=\"arx-code-wrap\"><span class=\"arx-code-label\">Exemple: calcular t per a una classe<\/span>\r\n<div class=\"arx-code\"><code><span class=\"cm\"># Classe CE-6: {P008, P009} \u2014 homes 60\u201370 anys<\/span>\r\n<span class=\"cm\"># Diagn\u00f2stics: Cardiopatia isqu\u00e8mica (50%), Ins. card\u00edaca (50%)<\/span>\r\n\r\nDistribuci\u00f3 global de refer\u00e8ncia:\r\n  IC: <span class=\"val\">30%<\/span>   FA: <span class=\"val\">20%<\/span>   HTA: <span class=\"val\">20%<\/span>   Card.Isq: <span class=\"val\">30%<\/span>\r\n\r\nDistribuci\u00f3 local CE-6:\r\n  IC: <span class=\"val\">50%<\/span>   FA: <span class=\"val\">0%<\/span>    HTA: <span class=\"val\">0%<\/span>    Card.Isq: <span class=\"val\">50%<\/span>\r\n\r\n<span class=\"cm\"># EMD = suma de difer\u00e8ncies absolutes \/ 2<\/span>\r\n|50-30| + |0-20| + |0-20| + |50-30| = 80\r\nEMD = 80 \/ 2 = <span class=\"val\">0.40<\/span>\r\n\r\nCompleix t=0.20? <span class=\"err\">NO (0.40 &gt; 0.20)<\/span>\r\nCompleix t=0.50? <span class=\"ok\">S\u00cd (0.40 \u2264 0.50)<\/span><\/code><\/div>\r\n<\/div>\r\n<h3>Quin valor de t triar?<\/h3>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>Valor de t<\/th>\r\n<th>Protecci\u00f3<\/th>\r\n<th>P\u00e8rdua d&#8217;info<\/th>\r\n<th>Cas d&#8217;\u00fas<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td><strong>t = 0.05\u20130.10<\/strong><\/td>\r\n<td><span class=\"badge-ok\">Molt alta<\/span><\/td>\r\n<td>Molt alta<\/td>\r\n<td>Dades extremadament sensibles (VIH, salut mental)<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>t = 0.15\u20130.20<\/strong><\/td>\r\n<td><span class=\"badge-ok\">Alta<\/span><\/td>\r\n<td>Moderada\u2013Alta<\/td>\r\n<td>Dades cl\u00edniques per a publicaci\u00f3 p\u00fablica<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>t = 0.25\u20130.35<\/strong><\/td>\r\n<td><span class=\"badge-warn\">Moderada<\/span><\/td>\r\n<td>Baixa\u2013Moderada<\/td>\r\n<td>Dades de recerca per a \u00fas intern<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"gui-arx\">Pas 6: Usar la GUI d&#8217;ARX pas a pas<\/h2>\r\n<p>ARX organitza el flux de treball en <strong>quatre perspectives<\/strong> visuals. Les recorres en ordre: Configuraci\u00f3 \u2192 Exploraci\u00f3 \u2192 Utilitat \u2192 Riscos.<\/p>\r\n<h3>Instal\u00b7laci\u00f3 en 2 minuts<\/h3>\r\n<ol class=\"arx-steps\">\r\n<li>\r\n<p><strong>Descarregar<\/strong> \u2014 Ves a <code>arx.deidentifier.org\/downloads<\/code> i baixa el ZIP de l&#8217;\u00faltima versi\u00f3 estable.<\/p>\r\n<\/li>\r\n<li>\r\n<p><strong>Verificar Java<\/strong> \u2014 Obre un terminal i escriu <code>java -version<\/code>. Necessites Java 11+. Si no el tens, descarrega&#8217;l de <code>adoptium.net<\/code> (gratu\u00eft).<\/p>\r\n<\/li>\r\n<li>\r\n<p><strong>Executar<\/strong> \u2014 Fes doble clic sobre <code>arxanonymizer.jar<\/code>. Si no s&#8217;obre, des del terminal: <code>java -jar arxanonymizer.jar<\/code><\/p>\r\n<\/li>\r\n<\/ol>\r\n<h3>Perspectiva 1: Configuraci\u00f3<\/h3>\r\n<p>Aqu\u00ed defineixes el dataset i les jerarquies de generalitzaci\u00f3.<\/p>\r\n<ol class=\"arx-steps\">\r\n<li>\r\n<p><strong>Importar el CSV<\/strong> \u2014 <em>File &gt; Import Data &gt; CSV File<\/em>. Configura separador (coma), encoding (UTF-8) i activa &#8220;First row contains header&#8221;.<\/p>\r\n<\/li>\r\n<li>\r\n<p><strong>Assignar tipus<\/strong> \u2014 Clic dret sobre cada columna &gt; Attribute type. Marca els QI com a Quasi-identifying, el diagn\u00f2stic com a Sensitive, els identificadors directes com a Identifying.<\/p>\r\n<\/li>\r\n<li>\r\n<p><strong>Crear jerarquies<\/strong> \u2014 Per a cada QI, clic dret &gt; Edit Hierarchy. Per a l&#8217;edat usa &#8220;Order-based&#8221; amb intervals de 10 anys. Per al codi postal usa &#8220;Masking-based&#8221; (080** \u2192 08*** \u2192 *).<\/p>\r\n<\/li>\r\n<li>\r\n<p><strong>Configurar el model<\/strong> \u2014 Panel dret &gt; Add criterion. Afegeix k-Anonymity (k=5), Distinct l-Diversity (l=3, atribut=Diagn\u00f2stic) i opcionalment t-Closeness (t=0.20).<\/p>\r\n<\/li>\r\n<\/ol>\r\n<div class=\"arx-code-wrap\"><span class=\"arx-code-label\">jerarquia_cp.csv \u2014 exemple<\/span>\r\n<div class=\"arx-code\"><code><span class=\"cm\"># Format: valor_original, nivell1, nivell2, supressi\u00f3_total<\/span>\r\n<span class=\"val\">08001<\/span>,080**,08***,*\r\n<span class=\"val\">08002<\/span>,080**,08***,*\r\n<span class=\"val\">08010<\/span>,080**,08***,*\r\n<span class=\"val\">08015<\/span>,080**,08***,*\r\n<span class=\"val\">08020<\/span>,080**,08***,*\r\n<span class=\"val\">08030<\/span>,080**,08***,*<\/code><\/div>\r\n<\/div>\r\n<h3>Perspectiva 2: Exploraci\u00f3 (el lattice)<\/h3>\r\n<p>ARX construeix un <strong>lattice de transformacions<\/strong>: un graf on cada node \u00e9s una combinaci\u00f3 possible de nivells de generalitzaci\u00f3. Els nodes <span style=\"color: #27ae60; font-weight: bold;\">verds<\/span> compleixen els criteris, els <span style=\"color: #e74c3c; font-weight: bold;\">vermells<\/span> no. El node recomanat (millor utilitat + privacitat) apareix ressaltat.<\/p>\r\n<div class=\"arx-callout callout-blue\"><span class=\"arx-callout-label\">Consell pr\u00e0ctic<\/span>\r\n<p>Si el lattice t\u00e9 molts nodes vermells, el teu dataset \u00e9s massa petit o els QI massa espec\u00edfics per a la k escollida. Prova a <strong>reduir k en un nivell<\/strong> o a <strong>augmentar la granularitat de les jerarquies<\/strong> (intervals d&#8217;edat de 20 anys en lloc de 10).<\/p>\r\n<\/div>\r\n<h3>Perspectiva 3: Utilitat<\/h3>\r\n<p>Comprova que el dataset anonimitzat segueix sent v\u00e0lid per a les teves an\u00e0lisis. ARX mostra histogrames comparatius (original vs. anonimitzat) i les m\u00e8triques clau:<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>M\u00e8trica<\/th>\r\n<th>Valor ideal<\/th>\r\n<th>Alerta si&#8230;<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Non-Uniform Entropy Loss<\/td>\r\n<td>&lt; 30%<\/td>\r\n<td>&gt; 50%: les an\u00e0lisis estad\u00edstiques poden ser inv\u00e0lides<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Registres suprimits<\/td>\r\n<td>&lt; 10%<\/td>\r\n<td>&gt; 20%: revisa les jerarquies o redueix k<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Mida mitja de les classes<\/td>\r\n<td>Pr\u00f2xima a k<\/td>\r\n<td>Molt major que k: possible sobreprotecci\u00f3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<h3>Perspectiva 4: Riscos<\/h3>\r\n<p>Aqu\u00ed \u00e9s on demostres al CEI i als revisors que la protecci\u00f3 \u00e9s real. ARX calcula els tres riscos de re-identificaci\u00f3:<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>Indicador<\/th>\r\n<th>Model<\/th>\r\n<th>Acceptable per publicaci\u00f3<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>Highest risk (individual)<\/td>\r\n<td>Prosecutor<\/td>\r\n<td><span class=\"badge-ok\">&lt; 0.33<\/span><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Success rate (re-id)<\/td>\r\n<td>Journalist<\/td>\r\n<td><span class=\"badge-ok\">&lt; 0.20<\/span><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Expected risk<\/td>\r\n<td>Marketer<\/td>\r\n<td><span class=\"badge-ok\">&lt; 0.10<\/span><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/>\r\n<h2 id=\"resum\">Resum i checklist final<\/h2>\r\n<p>Abans de donar per acabat el proc\u00e9s d&#8217;anonimitzaci\u00f3, comprova cada punt d&#8217;aquesta llista:<\/p>\r\n<div class=\"arx-table-wrap\">\r\n<table class=\"arx-table\">\r\n<thead>\r\n<tr>\r\n<th>#<\/th>\r\n<th>Acci\u00f3<\/th>\r\n<th>Documentat?<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td>1<\/td>\r\n<td>Atributs classificats i validats amb el director\/a i el CEI<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>2<\/td>\r\n<td>Jerarquies de generalitzaci\u00f3 creades i exportades com a CSV<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>3<\/td>\r\n<td>Model de privacitat configurat: k \u2265 5 + l \u2265 3 + t \u2264 0.20<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>4<\/td>\r\n<td>P\u00e8rdua d&#8217;informaci\u00f3 &lt; 30% i registres suprimits &lt; 10%<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>5<\/td>\r\n<td>Riscos de re-identificaci\u00f3 dins dels llindars acceptables<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>6<\/td>\r\n<td>Fitxer .arx guardat per a reproductibilitat<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>7<\/td>\r\n<td>Secci\u00f3 metodol\u00f2gica d&#8217;anonimitzaci\u00f3 documentada<\/td>\r\n<td>\u2610<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<div class=\"arx-callout callout-green\"><span class=\"arx-callout-label\">Combinaci\u00f3 recomanada per a dades cl\u00edniques<\/span>\r\n<ul>\r\n<li><strong>k = 5<\/strong> (k-anonimitat) per a publicaci\u00f3 acad\u00e8mica est\u00e0ndard<\/li>\r\n<li><strong>Entropy 3-diversity<\/strong> per a diagn\u00f2stics amb distribuci\u00f3 no uniforme<\/li>\r\n<li><strong>t = 0.20<\/strong> (t-closeness) per a dades amb diagn\u00f2stics rars o molt prevalents<\/li>\r\n<li>Model d&#8217;atacant: <strong>Journalist<\/strong> com a m\u00ednim per a publicaci\u00f3 oberta<\/li>\r\n<\/ul>\r\n<\/div>\r\n<!-- \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 --><hr class=\"arx-divider\" \/><!-- \u2500\u2500 CTA \u2500\u2500 -->\r\n<div class=\"arx-cta\">\r\n<h3>Continua aprenent sobre privacitat de dades en recerca<\/h3>\r\n<p>Tens el document complet amb exemples guiats, exercicis i plantilles, lliure per descarregar.<\/p>\r\n<a class=\"arx-cta-btn\" href=\"#\">Descarregar la guia completa (Word)<\/a> <a class=\"arx-cta-btn outline\" href=\"https:\/\/arx.deidentifier.org\" target=\"_blank\" rel=\"noopener\">Descarregar ARX gratu\u00eftament<\/a><\/div>\r\n<hr class=\"arx-divider\" \/>\r\n<p style=\"font-size: 0.85rem; color: #888;\"><strong>Refer\u00e8ncies:<\/strong> Prasser F. et al. (2020). <em>Flexible Data Anonymization Using ARX<\/em>. Software: Practice and Experience. \u00b7 Sweeney L. (2002). <em>k-Anonymity: A Model for Protecting Privacy<\/em>. International Journal of Uncertainty. \u00b7 Li N. et al. (2007). <em>t-Closeness: Privacy Beyond k-Anonymity and l-Diversity<\/em>. IEEE ICDE. \u00b7 Machanavajjhala A. et al. (2007). <em>l-Diversity: Privacy Beyond k-Anonymity<\/em>. ACM TKDD.<\/p>\r\n<\/article>\r\n","protected":false},"excerpt":{"rendered":"<p>Tot el que necessites saber sobre k-anonimitat, l-diversity i t-closeness, amb exemples reals pas a pas i consells per comen\u00e7ar a fer servir l&#8217;eina d&#8217;anonimitzaci\u00f3 ARX. Recerca &amp; MetodologiaTemps de lectura: ~15 minNivell: IntermediEina: ARX v3.9+ Si treballes amb dades de pacients, enquestes o qualsevol dataset que contingui informaci\u00f3 personal, anonimitzar correctament \u00e9s una obligaci\u00f3 &hellip; <a href=\"https:\/\/coneixement.info\/blog\/arx-anonymization-tool-guia-practica-per-anonimitzar-dades-de-recerca-2\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">ARX Anonymization Tool: guia pr\u00e0ctica per anonimitzar dades de recerca<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","footnotes":""},"categories":[7,27,5,6],"tags":[50,46,49,51,53,47,52,48],"class_list":["post-776","post","type-post","status-publish","format-standard","hentry","category-health","category-learning","category-opensource","category-science","tag-anonimitzacio","tag-arx","tag-ciencia-de-dades","tag-doctorat","tag-k-anonimitat","tag-privacitat-de-dades","tag-recerca-clinica","tag-rgpd"],"_links":{"self":[{"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/posts\/776","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/comments?post=776"}],"version-history":[{"count":3,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/posts\/776\/revisions"}],"predecessor-version":[{"id":780,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/posts\/776\/revisions\/780"}],"wp:attachment":[{"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/media?parent=776"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/categories?post=776"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coneixement.info\/blog\/wp-json\/wp\/v2\/tags?post=776"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}