{"id":37167,"date":"2025-12-28T09:30:47","date_gmt":"2025-12-28T06:30:47","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/aws-clean-rooms-ile-sentetik-veri-uretimine-baslangic-analitik-uygulamalar\/"},"modified":"2025-12-28T09:30:47","modified_gmt":"2025-12-28T06:30:47","slug":"aws-clean-rooms-ile-sentetik-veri-uretimine-baslangic-analitik-uygulamalar","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/aws-clean-rooms-ile-sentetik-veri-uretimine-baslangic-analitik-uygulamalar\/","title":{"rendered":"AWS Clean Rooms ile Sentetik Veri \u00dcretimine Ba\u015flang\u0131\u00e7: Analitik Uygulamalar"},"content":{"rendered":"<h2>AWS Clean Rooms ile Sentetik Veri \u00dcretimine Ba\u015flang\u0131\u00e7: Analitik Uygulamalar<\/h2>\n<p>Sentetik veri \u00fcretimi, g\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda gizlilik endi\u015feleri ve veri eri\u015fim k\u0131s\u0131tlamalar\u0131yla ba\u015fa \u00e7\u0131kmak i\u00e7in kritik bir \u00e7\u00f6z\u00fcm haline gelmi\u015ftir. Ger\u00e7ek verilere benzer istatistiksel \u00f6zelliklere sahip, ancak hi\u00e7bir ger\u00e7ek ki\u015fisel bilgi i\u00e7ermeyen sentetik veriler, geli\u015ftiricilerin, analistlerin ve veri bilimcilerin g\u00fcvenli bir ortamda inovasyon yapmas\u0131na olanak tan\u0131r. Bu makale, \u00f6zellikle AWS Clean Rooms gibi g\u00fc\u00e7l\u00fc bir ara\u00e7 kullanarak sentetik veri \u00fcretiminin temellerine odaklanacak ve analitik uygulamalar i\u00e7in nas\u0131l kullan\u0131labilece\u011fini ad\u0131m ad\u0131m a\u00e7\u0131klayacakt\u0131r.<\/p>\n<h2>Sentetik Veriye Giri\u015f: Neden ve Nas\u0131l?<\/h2>\n<h3>Sentetik Veri Nedir?<\/h3>\n<p>Sentetik veri, ger\u00e7ek verinin istatistiksel \u00f6zelliklerini, desenlerini ve ili\u015fkilerini taklit eden, ancak ger\u00e7ek ki\u015fisel bilgileri i\u00e7ermeyen yapay olarak olu\u015fturulmu\u015f veri setleridir. Bu veriler, orijinal veri setindeki bireyleri tan\u0131mlamak i\u00e7in kullan\u0131lamaz, bu da onlar\u0131 gizlilik odakl\u0131 senaryolar i\u00e7in ideal k\u0131lar. Sentetik veriler, genellikle karma\u015f\u0131k algoritmalar ve makine \u00f6\u011frenimi modelleri (\u00f6rne\u011fin, \u00dcretken \u00c7eki\u015fmeli A\u011flar &#8211; GAN&#8217;lar) kullan\u0131larak ger\u00e7ek verilerden \u00f6\u011frenilerek \u00fcretilir.<\/p>\n<h3>Sentetik Veriye Neden \u0130htiya\u00e7 Duyar\u0131z?<\/h3>\n<p>Sentetik veriye olan ihtiya\u00e7, modern veri d\u00fcnyas\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131\u011f\u0131 \u00e7e\u015fitli zorluklardan kaynaklanmaktad\u0131r:<\/p>\n<ul>\n<li><strong>Gizlilik ve Uyum:<\/strong> GDPR, KVKK gibi kat\u0131 veri gizlili\u011fi d\u00fczenlemeleri, hassas ger\u00e7ek verilerin payla\u015f\u0131m\u0131n\u0131 ve kullan\u0131m\u0131n\u0131 k\u0131s\u0131tlar. Sentetik veri, bu d\u00fczenlemelere uyumu kolayla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>Veri Eri\u015fimi ve Payla\u015f\u0131m\u0131:<\/strong> Kurulu\u015flar aras\u0131 veri payla\u015f\u0131m\u0131 veya dahili departmanlar aras\u0131 veri eri\u015fimi, gizlilik endi\u015feleri nedeniyle s\u0131k\u00e7a engellenir. Sentetik veri, bu engelleri a\u015farak i\u015fbirli\u011fini te\u015fvik eder.<\/li>\n<li><strong>Geli\u015ftirme ve Test Ortamlar\u0131:<\/strong> Yaz\u0131l\u0131m geli\u015ftiricileri ve veri bilimcileri, ger\u00e7ek verilere eri\u015fim k\u0131s\u0131tl\u0131 oldu\u011funda veya \u00fcretim verilerini kullanmak riskli oldu\u011funda, sentetik verilerle g\u00fcvenli bir \u015fekilde geli\u015ftirme ve test yapabilirler.<\/li>\n<li><strong>Veri Eksikli\u011fi ve Dengeleme:<\/strong> Baz\u0131 senaryolarda yeterli ger\u00e7ek veri bulunmayabilir veya veri setleri dengesiz olabilir. Sentetik veri, bu bo\u015fluklar\u0131 doldurmak ve model performans\u0131n\u0131 art\u0131rmak i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<h3>Sentetik Veri \u00dcretim Y\u00f6ntemleri<\/h3>\n<p>Sentetik veri \u00fcretiminde farkl\u0131 yakla\u015f\u0131mlar bulunmaktad\u0131r:<\/p>\n<ul>\n<li><strong>Kurallara Dayal\u0131 Y\u00f6ntemler:<\/strong> \u00d6nceden tan\u0131mlanm\u0131\u015f kurallar ve da\u011f\u0131l\u0131mlar kullan\u0131larak veri \u00fcretilir. Basit ve h\u0131zl\u0131d\u0131r ancak ger\u00e7ek verinin karma\u015f\u0131k ili\u015fkilerini tam olarak yakalayamaz.<\/li>\n<li><strong>\u0130statistiksel Modelleme:<\/strong> Ger\u00e7ek verinin istatistiksel \u00f6zelliklerini (ortalama, varyans, korelasyon) \u00f6\u011frenen modeller kullan\u0131l\u0131r. \u00d6rnekler aras\u0131nda regresyon modelleri, karar a\u011fa\u00e7lar\u0131 veya \u00f6zel istatistiksel algoritmalar bulunur.<\/li>\n<li><strong>Makine \u00d6\u011frenimi Tabanl\u0131 Y\u00f6ntemler:<\/strong> En geli\u015fmi\u015f y\u00f6ntemlerdir. \u00dcretken \u00c7eki\u015fmeli A\u011flar (GAN&#8217;lar), Varyasyonel Otomatik Kodlay\u0131c\u0131lar (VAE&#8217;ler) gibi derin \u00f6\u011frenme modelleri, ger\u00e7ek verinin karma\u015f\u0131k desenlerini ve da\u011f\u0131l\u0131mlar\u0131n\u0131 \u00f6\u011frenerek y\u00fcksek kaliteli sentetik veriler \u00fcretebilir. Bu y\u00f6ntemler genellikle daha fazla hesaplama g\u00fcc\u00fc ve uzmanl\u0131k gerektirir.<\/li>\n<\/ul>\n<h2>AWS Clean Rooms Nedir ve Sentetik Veriyle \u0130li\u015fkisi<\/h2>\n<h3>AWS Clean Rooms&#8217;a Genel Bak\u0131\u015f<\/h3>\n<p>AWS Clean Rooms, birden fazla taraf\u0131n kendi ham verilerini birbirleriyle payla\u015fmadan ortak analizler yapmas\u0131na olanak tan\u0131yan, g\u00fcvenli ve gizlilik odakl\u0131 bir AWS hizmetidir. Kurulu\u015flar, kendi veri setlerini Clean Rooms&#8217;a getirerek, \u00f6nceden tan\u0131mlanm\u0131\u015f ve onaylanm\u0131\u015f analiz kurallar\u0131 \u00e7er\u00e7evesinde di\u011fer kat\u0131l\u0131mc\u0131larla i\u015fbirli\u011fi yapabilirler. Bu, veri gizlili\u011fini korurken, pazar ara\u015ft\u0131rmas\u0131, reklam \u00f6l\u00e7\u00fcmleme, doland\u0131r\u0131c\u0131l\u0131k tespiti ve daha bir\u00e7ok alanda derinlemesine i\u00e7g\u00f6r\u00fcler elde etmeyi m\u00fcmk\u00fcn k\u0131lar.<\/p>\n<h3>Clean Rooms&#8217;un Temel \u00c7al\u0131\u015fma Prensibi<\/h3>\n<p>AWS Clean Rooms&#8217;un \u00e7al\u0131\u015fma prensibi olduk\u00e7a basittir ancak g\u00fc\u00e7l\u00fc gizlilik mekanizmalar\u0131na sahiptir:<\/p>\n<ol>\n<li><strong>Clean Room Olu\u015fturma:<\/strong> Bir kurulu\u015f (sahip), bir Clean Room olu\u015fturur ve di\u011fer kurulu\u015flar\u0131 (\u00fcyeler) bu odaya davet eder.<\/li>\n<li><strong>Veri Kaynaklar\u0131n\u0131 Ba\u011flama:<\/strong> Her \u00fcye, kendi AWS hesab\u0131ndaki veri kaynaklar\u0131n\u0131 (\u00f6rne\u011fin, Amazon S3, Amazon Redshift, AWS Lake Formation) Clean Room&#8217;a ba\u011flar. Veriler Clean Room&#8217;a ta\u015f\u0131nmaz, sadece sorgulanabilir hale gelir.<\/li>\n<li><strong>Analiz Kurallar\u0131n\u0131 Tan\u0131mlama:<\/strong> Clean Room sahibi, \u00fcyelerin veri \u00fczerinde \u00e7al\u0131\u015ft\u0131rabilece\u011fi SQL sorgular\u0131 i\u00e7in kat\u0131 analiz kurallar\u0131 (\u00f6rne\u011fin, izin verilen SQL fonksiyonlar\u0131, minimum grup boyutu, \u00e7\u0131kt\u0131da izin verilen s\u00fctunlar) tan\u0131mlar.<\/li>\n<li><strong>Sorgu \u00c7al\u0131\u015ft\u0131rma:<\/strong> \u00dcyeler, bu kurallara uygun SQL sorgular\u0131n\u0131 Clean Room i\u00e7inde \u00e7al\u0131\u015ft\u0131r\u0131r. Clean Rooms, sorguyu her bir \u00fcyenin kendi verisi \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131r ve sonu\u00e7lar\u0131 birle\u015ftirir.<\/li>\n<li><strong>Gizlilik Korumal\u0131 Sonu\u00e7lar:<\/strong> Sorgu sonu\u00e7lar\u0131, tan\u0131mlanm\u0131\u015f gizlilik kurallar\u0131na (\u00f6rne\u011fin, minimum grup boyutu e\u015fi\u011fi kar\u015f\u0131lanmazsa sonu\u00e7 d\u00f6nd\u00fcrmeme) uygun olarak filtrelenir ve yaln\u0131zca gizlili\u011fi korunmu\u015f \u00f6zet bilgiler payla\u015f\u0131l\u0131r.<\/li>\n<\/ol>\n<h3>Sentetik Veri \u00dcretiminde Clean Rooms&#8217;un Rol\u00fc<\/h3>\n<p>AWS Clean Rooms do\u011frudan sentetik veri \u00fcretmez. Ancak, sentetik veri \u00fcretimi s\u00fcrecinde kritik bir rol oynar:<\/p>\n<ul>\n<li><strong>G\u00fcvenli Veri Analizi:<\/strong> Sentetik veri modellerini e\u011fitmek i\u00e7in genellikle ger\u00e7ek verinin istatistiksel \u00f6zelliklerinin ve ili\u015fkilerinin detayl\u0131 analizi gerekir. Clean Rooms, bu analizleri birden fazla taraf\u0131n hassas verilerini do\u011frudan payla\u015fmadan g\u00fcvenli bir \u015fekilde yapmas\u0131na olanak tan\u0131r.<\/li>\n<li><strong>Model E\u011fitimi \u0130\u00e7in \u00d6zet Bilgiler:<\/strong> Clean Rooms i\u00e7inde \u00e7al\u0131\u015ft\u0131r\u0131lan sorgularla, sentetik veri modelini beslemek i\u00e7in gerekli olan korelasyonlar, da\u011f\u0131l\u0131mlar ve di\u011fer istatistiksel \u00f6zetler elde edilebilir. Bu \u00f6zetler daha sonra d\u0131\u015f bir sentetik veri \u00fcretim arac\u0131na girdi olarak verilir.<\/li>\n<li><strong>Sentetik Veri Kalite De\u011ferlendirmesi:<\/strong> \u00dcretilen sentetik verinin ger\u00e7ek veriye ne kadar benzedi\u011fini de\u011ferlendirmek i\u00e7in, Clean Rooms i\u00e7inde sentetik veri \u00fczerinde benzer analizler yap\u0131labilir ve ger\u00e7ek veriden elde edilen sonu\u00e7larla kar\u015f\u0131la\u015ft\u0131r\u0131labilir.<\/li>\n<li><strong>Sentetik Veri ile \u0130\u015fbirli\u011fi:<\/strong> Sentetik veri \u00fcretildikten sonra, bu veri setleri Clean Rooms i\u00e7inde i\u015f ortaklar\u0131yla g\u00fcvenli bir \u015fekilde payla\u015f\u0131labilir ve \u00fczerinde ortak analizler yap\u0131labilir.<\/li>\n<\/ul>\n<h2>AWS Clean Rooms ile Sentetik Veri \u00dcretiminin Temel Ad\u0131mlar\u0131<\/h2>\n<p>AWS Clean Rooms&#8217;u sentetik veri \u00fcretim s\u00fcrecine entegre etmek, birka\u00e7 temel ad\u0131mdan olu\u015fur. Bu ad\u0131mlar, ger\u00e7ek veriden de\u011ferli istatistiksel bilgileri g\u00fcvenli bir \u015fekilde \u00e7\u0131karmay\u0131 ve bu bilgileri sentetik veri \u00fcretimi i\u00e7in kullanmay\u0131 hedefler.<\/p>\n<h3>Ger\u00e7ek Veri Kaynaklar\u0131n\u0131n Belirlenmesi ve Haz\u0131rlanmas\u0131<\/h3>\n<p>\u0130lk ad\u0131m, sentetik veri \u00fcretimi i\u00e7in temel olu\u015fturacak ger\u00e7ek veri setlerini belirlemek ve haz\u0131rlamakt\u0131r. Bu veriler genellikle Amazon S3&#8217;te depolanan dosyalar, Amazon Redshift tablolar\u0131 veya di\u011fer AWS veri depolama hizmetlerinde bulunabilir.<\/p>\n<ul>\n<li><strong>Veri Tespiti:<\/strong> Hangi veri setlerinin sentetik veriye d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fini belirleyin. Bu, m\u00fc\u015fteri davran\u0131\u015flar\u0131, finansal i\u015flemler veya sa\u011fl\u0131k kay\u0131tlar\u0131 gibi hassas veriler olabilir.<\/li>\n<li><strong>Veri Temizli\u011fi ve \u00d6n \u0130\u015fleme:<\/strong> Eksik de\u011ferleri doldurun, hatalar\u0131 d\u00fczeltin ve verileri tutarl\u0131 bir formata getirin.<\/li>\n<li><strong>Anonimle\u015ftirme\/Pseudonimle\u015ftirme (Gerekliyse):<\/strong> Do\u011frudan tan\u0131mlay\u0131c\u0131lar\u0131 (\u00f6rne\u011fin, isim, e-posta) takma adlarla de\u011fi\u015ftirin veya kald\u0131r\u0131n. Bu, Clean Rooms&#8217;a girmeden \u00f6nce ek bir gizlilik katman\u0131 sa\u011flar.<\/li>\n<li><strong>Veri Format\u0131:<\/strong> Verilerin Clean Rooms taraf\u0131ndan desteklenen formatlarda (\u00f6rne\u011fin, CSV, Parquet) oldu\u011fundan emin olun.<\/li>\n<\/ul>\n<h3>AWS Clean Rooms Ortam\u0131n\u0131n Yap\u0131land\u0131r\u0131lmas\u0131<\/h3>\n<p>Veriler haz\u0131rland\u0131ktan sonra, AWS Clean Rooms ortam\u0131n\u0131 sentetik veri \u00fcretimi i\u00e7in yap\u0131land\u0131rmak gerekir.<\/p>\n<ol>\n<li><strong>Clean Room Olu\u015fturma:<\/strong> AWS Y\u00f6netim Konsolu \u00fczerinden yeni bir Clean Room olu\u015fturun. Bu, i\u015fbirli\u011fi yapaca\u011f\u0131n\u0131z sanal bir alan olacakt\u0131r.<\/li>\n<li><strong>\u00dcyeleri Davet Etme:<\/strong> E\u011fer sentetik veri \u00fcretimi i\u00e7in birden fazla taraf\u0131n verisine ihtiyac\u0131n\u0131z varsa, ilgili AWS hesaplar\u0131n\u0131 Clean Room&#8217;a \u00fcye olarak davet edin.<\/li>\n<li><strong>Tablolar\u0131 Yap\u0131land\u0131rma:<\/strong> Her \u00fcye, kendi AWS hesab\u0131ndaki ilgili veri setlerini Clean Room&#8217;daki tablolara ba\u011flar. Bu, Clean Room&#8217;un bu verilere eri\u015fimini sa\u011flar.<\/li>\n<li><strong>Analiz Kurallar\u0131n\u0131 Tan\u0131mlama:<\/strong> Bu ad\u0131m kritik \u00f6neme sahiptir. Sentetik veri modelini e\u011fitmek i\u00e7in hangi istatistiksel \u00f6zetlerin veya korelasyonlar\u0131n \u00e7\u0131kar\u0131labilece\u011fini belirleyen analiz kurallar\u0131n\u0131 (\u00f6rne\u011fin, minimum grup boyutu, izin verilen SQL fonksiyonlar\u0131, diferansiyel gizlilik ayarlar\u0131) dikkatlice tan\u0131mlay\u0131n. Bu kurallar, hassas bilgilerin s\u0131zmas\u0131n\u0131 \u00f6nler.<\/li>\n<\/ol>\n<h3>Sentetik Veri Modeli E\u011fitimi i\u00e7in Analiz<\/h3>\n<p>Clean Rooms ortam\u0131 kurulduktan sonra, sentetik veri modelini e\u011fitmek i\u00e7in gerekli istatistiksel bilgileri \u00e7\u0131karmak \u00fczere sorgular \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r.<\/p>\n<ul>\n<li><strong>\u0130statistiksel \u00d6zet Sorgular\u0131:<\/strong> Clean Room i\u00e7inde, veri setlerindeki anahtar de\u011fi\u015fkenler aras\u0131ndaki korelasyonlar\u0131, da\u011f\u0131l\u0131mlar\u0131, ortalamalar\u0131, medyanlar\u0131 ve di\u011fer istatistiksel \u00f6zetleri hesaplayan SQL sorgular\u0131 \u00e7al\u0131\u015ft\u0131r\u0131n. \u00d6rne\u011fin, m\u00fc\u015fteri demografisi ile sat\u0131n alma al\u0131\u015fkanl\u0131klar\u0131 aras\u0131ndaki ili\u015fkiler.<\/li>\n<li><strong>Gizlilik Korumal\u0131 \u00c7\u0131kt\u0131:<\/strong> Clean Rooms, tan\u0131mlanan analiz kurallar\u0131na uygun olarak sorgu sonu\u00e7lar\u0131n\u0131 d\u00f6nd\u00fcr\u00fcr. Bu sonu\u00e7lar, do\u011frudan ki\u015fisel tan\u0131mlay\u0131c\u0131lar i\u00e7ermez ve gizlilik e\u015fiklerini kar\u015f\u0131lar.<\/li>\n<li><strong>Veri Deseni \u00c7\u0131kar\u0131m\u0131:<\/strong> Elde edilen \u00f6zet bilgiler, ger\u00e7ek verideki temel desenleri, ili\u015fkileri ve da\u011f\u0131l\u0131mlar\u0131 anlamak i\u00e7in kullan\u0131l\u0131r. Bu, sentetik veri modelinin bu desenleri taklit etmesini sa\u011flar.<\/li>\n<\/ul>\n<h3>Sentetik Veri \u00dcretimi ve Entegrasyonu<\/h3>\n<p>Elde edilen analiz sonu\u00e7lar\u0131, sentetik veri \u00fcretimini ger\u00e7ekle\u015ftirecek d\u0131\u015f bir araca girdi olarak verilir.<\/p>\n<ul>\n<li><strong>Sentetik Veri \u00dcretim Arac\u0131 Se\u00e7imi:<\/strong> AWS SageMaker Studio&#8217;da bir Python beti\u011fi, \u00f6zel bir makine \u00f6\u011frenimi modeli veya \u00fc\u00e7\u00fcnc\u00fc taraf bir sentetik veri \u00fcretim arac\u0131 (\u00f6rne\u011fin, Gretel, Mostly AI) kullan\u0131labilir.<\/li>\n<li><strong>Model E\u011fitimi:<\/strong> Clean Rooms&#8217;tan elde edilen istatistiksel \u00f6zetler ve desen bilgileri kullan\u0131larak sentetik veri modeli e\u011fitilir. Model, ger\u00e7ek verinin yap\u0131s\u0131n\u0131 \u00f6\u011frenir ve bu yap\u0131ya uygun yeni, yapay veri noktalar\u0131 \u00fcretir.<\/li>\n<li><strong>Sentetik Veriyi Y\u00fckleme:<\/strong> \u00dcretilen sentetik veri, analiz ve kullan\u0131m i\u00e7in tekrar AWS ortam\u0131na (\u00f6rne\u011fin, Amazon S3&#8217;e bir Parquet dosyas\u0131 olarak veya Amazon Redshift&#8217;e bir tablo olarak) y\u00fcklenir.<\/li>\n<\/ul>\n<h2>Pratik Bir Senaryo: AWS Clean Rooms \u00dczerinde Sentetik Veri Ak\u0131\u015f\u0131<\/h2>\n<p>Bu b\u00f6l\u00fcmde, AWS Clean Rooms&#8217;un sentetik veri \u00fcretim s\u00fcrecinde nas\u0131l kullan\u0131labilece\u011fini somut bir senaryo \u00fczerinden inceleyece\u011fiz.<\/p>\n<h3>Senaryo Tan\u0131m\u0131: Pazarlama Analizi \u0130\u00e7in Sentetik Veri<\/h3>\n<p>Bir perakende \u015firketi (\u015eirket A) ve bir reklam ajans\u0131 (\u015eirket B), m\u00fc\u015fteri sat\u0131n alma verileri ile kampanya etkile\u015fim verilerini birle\u015ftirmeden, ancak bu veriler aras\u0131ndaki korelasyonlar\u0131 anlayarak pazarlama kampanyalar\u0131n\u0131 optimize etmek istemektedir. Hedef, hassas m\u00fc\u015fteri verilerini do\u011frudan payla\u015fmadan, sentetik bir veri seti \u00fczerinde yeni kampanya stratejileri geli\u015ftirmektir.<\/p>\n<h3>Veri Sa\u011flay\u0131c\u0131 Rol\u00fc ve Clean Room Yap\u0131land\u0131rmas\u0131<\/h3>\n<ol>\n<li><strong>\u015eirket A (Perakendeci):<\/strong> M\u00fc\u015fteri sat\u0131n alma ge\u00e7mi\u015fi, \u00fcr\u00fcn kategorileri, harcama miktarlar\u0131 gibi verileri Amazon Redshift&#8217;te tutuyor.<\/li>\n<li><strong>\u015eirket B (Reklam Ajans\u0131):<\/strong> M\u00fc\u015fteri reklam etkile\u015fimleri, t\u0131klama oranlar\u0131, kampanya ID&#8217;leri gibi verileri Amazon S3&#8217;te tutuyor.<\/li>\n<li><strong>Clean Room Olu\u015fturma:<\/strong> \u015eirket A, bir AWS Clean Room olu\u015fturur ve \u015eirket B&#8217;yi \u00fcye olarak davet eder.<\/li>\n<li><strong>Tablo Ba\u011flant\u0131lar\u0131:<\/strong> Her iki \u015firket de kendi anonimle\u015ftirilmi\u015f m\u00fc\u015fteri ID&#8217;leri (\u00f6rne\u011fin, hashlenmi\u015f ID&#8217;ler) \u00fczerinden verilerini Clean Room&#8217;daki ilgili tablolara ba\u011flar.<\/li>\n<li><strong>Analiz Kurallar\u0131:<\/strong> \u015eirket A, a\u015fa\u011f\u0131daki gibi analiz kurallar\u0131 tan\u0131mlar:\n<ul>\n<li>Yaln\u0131zca belirli agregasyon fonksiyonlar\u0131na (COUNT, SUM, AVG) izin verilir.<\/li>\n<li>Ortak anahtar (anonim m\u00fc\u015fteri ID) \u00fczerinden birle\u015ftirme (JOIN) i\u015flemine izin verilir.<\/li>\n<li>Herhangi bir sorgu \u00e7\u0131kt\u0131s\u0131, en az 50 benzersiz m\u00fc\u015fteri ID&#8217;si i\u00e7eren gruplar i\u00e7in sonu\u00e7 d\u00f6nd\u00fcrmelidir (minimum grup boyutu).<\/li>\n<li>\u00c7\u0131kt\u0131da yaln\u0131zca kampanya ID&#8217;si, \u00fcr\u00fcn kategorisi ve agregasyon sonu\u00e7lar\u0131 gibi \u00f6zet bilgiler bulunabilir, do\u011frudan m\u00fc\u015fteri bilgileri olamaz.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h3>Sentetik Veri \u00dcretimi \u0130\u00e7in Sorgular ve Analiz<\/h3>\n<p>\u015eirket B (veya \u015eirket A), Clean Room i\u00e7inde, her iki veri setindeki ortak anonim m\u00fc\u015fteri ID&#8217;leri \u00fczerinden istatistiksel \u00f6zetler \u00e7\u0131karan sorgular \u00e7al\u0131\u015ft\u0131r\u0131r. Bu \u00f6zetler, sentetik veri modelini beslemek i\u00e7in kullan\u0131lacakt\u0131r.<\/p>\n<p>\u00d6rnek bir sorgu:<\/p>\n<pre><code class=\"language-sql\">\nSELECT\n    c.campaign_id,\n    p.product_category,\n    COUNT(DISTINCT c.anon_customer_id) AS unique_customers_engaged,\n    AVG(p.purchase_value) AS avg_purchase_value_after_campaign\nFROM\n    clean_room_table_company_b c -- Reklam Ajans\u0131 verisi\nJOIN\n    clean_room_table_company_a p ON c.anon_customer_id = p.anon_customer_id -- Perakendeci verisi\nWHERE\n    c.campaign_date BETWEEN '2023-01-01' AND '2023-03-31'\nGROUP BY\n    c.campaign_id, p.product_category\nHAVING\n    COUNT(DISTINCT c.anon_customer_id) >= 50; -- Tan\u0131mlanan gizlilik kural\u0131\n<\/pre>\n<p><\/code><\/p>\n<p>Bu sorgu, belirli bir kampanya d\u00f6neminde hangi \u00fcr\u00fcn kategorilerinin hangi kampanyalarla daha \u00e7ok etkile\u015fimde bulundu\u011funu ve bu etkile\u015fimlerin ortalama sat\u0131n alma de\u011ferini, gizlilik e\u015fiklerini koruyarak \u00f6zetler. Elde edilen bu \u00f6zet tablolar, daha sonra bir sentetik veri \u00fcretim modelini (\u00f6rne\u011fin, AWS SageMaker'da e\u011fitilmi\u015f bir GAN modeli) beslemek i\u00e7in kullan\u0131l\u0131r. Model, bu \u00f6zetlerden \u00f6\u011frenerek, ger\u00e7ek verinin istatistiksel \u00f6zelliklerini taklit eden yeni, sentetik m\u00fc\u015fteri-kampanya-sat\u0131n alma verileri \u00fcretecektir.<\/p>\n<h3>\u00dcretilen Sentetik Verinin Kullan\u0131m\u0131 ve Faydalar\u0131<\/h3>\n<p>\u00dcretilen sentetik veri seti, her iki \u015firket taraf\u0131ndan da g\u00fcvenli bir \u015fekilde kullan\u0131labilir:<\/p>\n<ul>\n<li><strong>Yeni Kampanyalar\u0131 Test Etme:<\/strong> \u015eirket B, sentetik veri \u00fczerinde farkl\u0131 kampanya hedefleme stratejilerini test edebilir.<\/li>\n<li><strong>Pazarlama Modelleri Geli\u015ftirme:<\/strong> \u015eirket A, sentetik veriyi kullanarak m\u00fc\u015fteri segmentasyonu veya sat\u0131n alma tahmini modelleri geli\u015ftirebilir.<\/li>\n<li><strong>\u0130\u015f Ortaklar\u0131yla Payla\u015f\u0131m:<\/strong> Sentetik veri, \u00fc\u00e7\u00fcnc\u00fc taraf analiz firmalar\u0131yla veya di\u011fer i\u015f ortaklar\u0131yla, ger\u00e7ek verinin hassasiyeti olmadan g\u00fcvenli bir \u015fekilde payla\u015f\u0131labilir.<\/li>\n<\/ul>\n<p>Bu senaryo, AWS Clean Rooms'un birden fazla taraf\u0131n gizlili\u011fini koruyarak sentetik veri \u00fcretimi i\u00e7in gerekli temel analizleri nas\u0131l kolayla\u015ft\u0131rd\u0131\u011f\u0131n\u0131 g\u00f6stermektedir.<\/p>\n<h2>G\u00fcvenlik, Gizlilik ve Uyum: Sentetik Veri ve Clean Rooms<\/h2>\n<p>Sentetik veri ve AWS Clean Rooms'un birle\u015fimi, veri gizlili\u011fi ve g\u00fcvenli\u011fi konusunda \u00f6nemli avantajlar sunar,<\/p>\n","protected":false},"excerpt":{"rendered":"Sentetik veri \u00fcretimi, g\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda gizlilik endi\u015feleri ve veri eri\u015fim k\u0131s\u0131tlamalar\u0131yla ba\u015fa \u00e7\u0131kmak i\u00e7in kritik bir \u00e7\u00f6&#8230;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1406],"tags":[],"class_list":{"0":"post-37167","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-aws","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - 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