{"id":4662,"date":"2021-06-28T11:27:50","date_gmt":"2021-06-28T09:27:50","guid":{"rendered":"https:\/\/www.arcadsoftware.com\/dot\/?p=4662"},"modified":"2025-12-09T10:29:32","modified_gmt":"2025-12-09T09:29:32","slug":"masquage-des-donnees-et-anonymisation-comprendre-les-differents-algorithmes","status":"publish","type":"post","link":"https:\/\/www.arcadsoftware.fr\/dot\/ressources\/blog\/masquage-des-donnees-et-anonymisation-comprendre-les-differents-algorithmes\/","title":{"rendered":"Masquage et anonymisation des donn\u00e9es : 6 types d’algorithmes pour prot\u00e9ger vos donn\u00e9es"},"content":{"rendered":"

\"Banner<\/span><\/div><\/div><\/div><\/div><\/div>

Masquage et anonymisation des donn\u00e9es :
\ncomprendre les diff\u00e9rents algorithmes<\/h2><\/div>

Le masquage<\/strong> et l\u2019anonymisation des donn\u00e9es<\/strong> repr\u00e9sentent des piliers essentiels de la protection des donn\u00e9es sensibles<\/strong>, notamment dans le cadre du RGPD<\/a><\/strong>. Ces techniques permettent de modifier un jeu de donn\u00e9es afin de le rendre non identifiable, tout en pr\u00e9servant sa valeur pour les analyses ou les tests.<\/p>\n

Selon la CNIL, l\u2019anonymisation vise \u00e0 rendre impossible l\u2019identification d\u2019une personne \u00e0 partir de ses donn\u00e9es.<\/p>\n

Dans cet article, nous d\u00e9taillons les principaux algorithmes de Data Masking<\/strong> utilis\u00e9s pour prot\u00e9ger les informations tout en maintenant la coh\u00e9rence et l\u2019utilit\u00e9 des donn\u00e9es.<\/p>\n<\/div>

Les types d\u2019algorithmes de masquage de donn\u00e9es<\/h2><\/div>

1. Les algorithmes de substitution : conserver une apparence authentique<\/h3><\/div>

Lors de l\u2019usage d\u2019un algorithme de substitution, certaines informations du jeu de donn\u00e9es principal se voient substitu\u00e9es par d\u2019autres. Les informations sont alors, d\u2019apparence, toujours r\u00e9elles, mais elles permettent d\u2019anonymiser et de prot\u00e9ger l\u2019identit\u00e9 des personnes du jeu de donn\u00e9es originales.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: Green \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Jones\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div><\/div>

2. Les algorithmes al\u00e9atoires : m\u00e9langer les donn\u00e9es<\/h3><\/div>

Avec cet algorithme, les caract\u00e8res de chaque colonne sont m\u00e9lang\u00e9s de mani\u00e8re al\u00e9atoire. De la sorte, il est tr\u00e8s difficile de retrouver les informations originales.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: Worbn \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Miths\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div><\/div>

3. Les algorithmes de variation num\u00e9rique : reproduire des donn\u00e9es r\u00e9alistes<\/h3><\/div>

\u00c0 l\u2019aide d\u2019un algorithme de variation de nombres et des dates, il est possible de cr\u00e9er un jeu de donn\u00e9es fictif se basant sur des informations chiffr\u00e9es du jeu de donn\u00e9es initiales. \u00c0 l\u2019aide de la mise en place d\u2019une fourchette num\u00e9rique significative (+\/- 10% par exemple), il est possible d\u2019afficher des r\u00e9sultats proches de la r\u00e9alit\u00e9, qui permettraient dans le m\u00eame temps de rendre le jeu de donn\u00e9es initial totalement impossible \u00e0 retrouver.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 102600
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 112500<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>

Donn\u00e9es personnelles et Anonymisation : 5 conseils pour mener \u00e0 bien son projet d\u2019anonymisation<\/p>\n<\/div>

T\u00e9l\u00e9charger le guide<\/span>T\u00e9l\u00e9charger le guide<\/span><\/div><\/i><\/a><\/div><\/div><\/div>

4. Les algorithmes de r\u00e9daction : remplacer artificiellement des donn\u00e9es<\/h3><\/div>

Pour rendre totalement anonyme un jeu de donn\u00e9es, il est possible d\u2019utiliser un algorithme de r\u00e9daction. Ce dernier vient remplacer toutes les donn\u00e9es r\u00e9elles par une cha\u00eene de caract\u00e8res constante ou al\u00e9atoire non reli\u00e9e. Autrement dit, il s\u2019agit d\u2019un algorithme de substitution o\u00f9 les informations ne semblent pas authentiques.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: xxxxx \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: xxxxx\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div><\/div>

5. Les algorithmes de masquage : conserver une base de donn\u00e9es utilisable<\/h3><\/div>

Peu diff\u00e9rent de l\u2019algorithme pr\u00e9c\u00e9dent, l\u2019algorithme de masquage permet de mettre en place une r\u00e9daction partielle, o\u00f9 certaines informations sont conserv\u00e9es lors de l\u2019anonymisation.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: Bxxxx \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Sxxxx\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div><\/div>

6. Les algorithmes personnalis\u00e9s : r\u00e9pondre \u00e0 des besoins sp\u00e9cifiques<\/h3><\/div>

Parfois, les algorithmes pr\u00e9c\u00e9dents ne sont pas suffisants ou ne permettent pas de r\u00e9pondre \u00e0 une demande sp\u00e9cifique. Il est alors possible de mettre en place des algorithmes personnalis\u00e9s. Ces derniers se font g\u00e9n\u00e9ralement sur demande des entreprises. Il est alors possible de demander, par exemple, d\u2019intervertir certaines informations des diff\u00e9rentes lignes pour rendre les donn\u00e9es anonymes.<\/p>\n

Exemple :<\/b><\/p>\n<\/div>

Jeu de donn\u00e9es initial<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 95000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 125000<\/p>\n<\/div><\/div><\/div>

<\/i><\/div><\/div>
<\/i><\/div><\/div>

Donn\u00e9es anonymis\u00e9es<\/h4><\/div>

Nom<\/strong>\u00a0: Brown \u2013 Salaire<\/strong>\u00a0: 125000
\nNom\u00a0<\/strong>: Smith\u00a0\u2013 Salaire<\/strong>\u00a0: 95000<\/p>\n<\/div><\/div><\/div><\/div>

Conclusion : prot\u00e9gez vos donn\u00e9es sans perdre leur valeur<\/h2><\/div>

Les algorithmes d\u2019anonymisation et de masquage<\/strong> permettent de s\u00e9curiser efficacement les donn\u00e9es sensibles tout en pr\u00e9servant leur utilit\u00e9. Chaque m\u00e9thode poss\u00e8de ses avantages et s\u2019adapte \u00e0 diff\u00e9rents contextes m\u00e9tiers. <\/span>L\u2019essentiel est de choisir la bonne approche selon vos besoins de <\/span>confidentialit\u00e9, conformit\u00e9 et performance<\/strong>.<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div>

Anonymisez vos donn\u00e9es avec DOT Anonymizer<\/p>\n<\/div>

D\u00e9couvrez DOT Anonymizer<\/span>D\u00e9couvrez DOT Anonymizer<\/span><\/div><\/i><\/a><\/div><\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"

D\u00e9couvrez 6 techniques de masquage et d\u2019anonymisation des donn\u00e9es : substitution, m\u00e9lange, variation num\u00e9rique, et plus encore.<\/p>\n","protected":false},"author":4,"featured_media":6241,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[71],"tags":[],"class_list":["post-4662","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\n6 algorithmes d'anonymisation pour prot\u00e9ger vos donn\u00e9es<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez 6 algorithmes de masquage et d\u2019anonymisation des donn\u00e9es : substitution, m\u00e9lange, variation num\u00e9rique, et plus encore.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.arcadsoftware.fr\/dot\/ressources\/blog\/masquage-des-donnees-et-anonymisation-comprendre-les-differents-algorithmes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"6 algorithmes d'anonymisation pour prot\u00e9ger vos donn\u00e9es\" \/>\n<meta property=\"og:description\" content=\"D\u00e9couvrez 6 algorithmes de masquage et d\u2019anonymisation des donn\u00e9es : substitution, m\u00e9lange, variation num\u00e9rique, et plus encore.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.arcadsoftware.fr\/dot\/ressources\/blog\/masquage-des-donnees-et-anonymisation-comprendre-les-differents-algorithmes\/\" \/>\n<meta property=\"og:site_name\" content=\"DOT - 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