I-Data Tokenization Vs Masking
Siphila embulungeni ekhuthazwa ubuchwepheshe obuqhubekayo, ukuvikela ulwazi olubucayi kubaluleke kakhulu. Izinhlangano kuzo zonke izimboni zibhekana nenselele yokuvikela idatha ebalulekile ngenkathi zisavumela ukusetshenziswa kwayo ekuhlaziyeni, ekucwaningeni nasekusebenzeni kwebhizinisi. Yilapho umqondo wokungaziwa kwedatha uqala khona ukusebenza. Izindlela ezimbili ezivelele kulo mkhakha ziyi I-Data Tokenization Vs Masking.
Kuyini I-Data Tokenization Vs Masking nethi Kungani Kubalulekile?
I-Data Tokenization Vs Masking bhekisela ezindleleni zokuguqula idatha ebucayi ibe yifomethi engafundeki kuyilapho ugcina ukusebenziseka kwayo.
- Ukwenza amathokheni kungena esikhundleni sedatha ebucayi ngamathokheni ahlukile, angabuyiseleki emuva. Kucabange njengokushintsha inombolo yekhadi lakho lesikweletu ukuze uthole uchungechunge olungahleliwe, olungasho lutho lwezinhlamvu. Leli thokheni lingase lisetshenziswe ekwenzeni izinto, kodwa inombolo yoqobo ihlala ifihliwe.
- Ukufihla kuhlanganisa ukushintsha noma ukufihla izingxenye zedatha ebucayi. Izindlela ezijwayelekile zokufihla ubuso zihlanganisa:
- Ukulungiselelwa Kwedatha: Akufaki amakholomu athile noma imigqa equkethe ulwazi olubucayi.
- Ukushova Idatha: Ukuhlela kabusha ukuhleleka kwezinto zedatha ukuze kuphazamiseke amaphethini.
- Ukuphazanyiswa Kwedatha: Sethula izinguquko ezincane, ezingahleliwe kumanani wedatha.
Bobabili I-Data Tokenization Vs Masking sebenzela izinjongo ezibalulekile:
- Ukuthobelana: Ukuthobela imithetho efana ne-GDPR ne-CCPA, egunyaza ukuvikelwa kwedatha yomuntu siqu.
- Ukuphepha: Ukunciphisa ubungozi bokuphulwa kwedatha kanye nethuba lokusebenzisa kabi ulwazi olubucayi.
- Ubumfihlo: Ukuvikela ukugcinwa kuyimfihlo kwabantu idatha yabo ecutshungulwayo.
- Ukuqhubeka Kwebhizinisi: Ukuqinisekisa ukuthi imisebenzi ebalulekile eqhutshwa yidatha ingaqhubeka ngaphandle kokubeka engcupheni ukuphepha.
Isimo Somhlaba Wangempela: Ukuguqula I-Data Tokenization Vs Masking Yempumelelo
Ake sicabangele isimo sokucatshangelwa esibandakanya i-Eversource Energy, inkampani yosizo. I-Eversource iqoqa inani elikhulu ledatha yekhasimende, okuhlanganisa ulwazi lomuntu siqu, amaphethini okusetshenziswa kwamandla, nemilando yokukhokha. Le datha ibalulekile ngezinjongo ezihlukahlukene, njenge:
- Ukulungiswa okuqagelayo: Ukuhlonza okungaba khona ukwehluleka kwemishini kanye nokuhlela ngokuqhubekayo ukulungiswa.
- Ukuhlukaniswa kwekhasimende: Ukuhlanganisa izinhlelo zokonga amandla nemikhankaso yokumaketha ngokwezidingo ezithile zamakhasimende.
- Ukutholwa kokukhwabanisa: Ukuhlonza kanye nokuvimbela imisebenzi yokukhwabanisa, efana nokuphazamisa imitha noma ukuntshontshwa kobunikazi.
Nokho, ukwabelana ngedatha yekhasimende ngalezi zinhloso kuletha ubumfihlo obubalulekile nezingozi zokuphepha. Ngokuqalisa I-Data Tokenization Vs Masking amasu, i-Eversource ingakwazi:
- Vikela ubumfihlo bekhasimende: Faka esikhundleni solwazi lomuntu siqu olubucayi njengezinombolo Zokuvikeleka Komphakathi namakheli ngamathokheni ahlukile, ukuvimbela ukufinyelela okungagunyaziwe noma ukudalulwa.
- Nika amandla imininingwane eshayelwa yidatha: Sebenzisa idatha efihliwe noma yamathokheni ukuze uhlaziye futhi wenze imodeli ngaphandle kokufaka engcupheni ubumfihlo bekhasimende.
- Ukuthobela imithethonqubo: Namathela kumazinga emboni kanye nezimfuneko zokulawula zokuvikela idatha.
Isibonelo, i-Eversource ingakwazi ukwenza amathokheni amagama ekhasimende namakheli emikhankaso yokumaketha kuyilapho isebenzisa idatha efihliwe yokusetshenziswa kwamandla kumamodeli okulungisa abikezelayo. Le ndlela ivumela inkampani ukuthi isebenzise amandla edatha yayo ngenkathi iqinisekisa ubumfihlo bekhasimende futhi inciphisa ubungozi bokuphulwa kwedatha.
I-Data Tokenization Vs Masking inikeza indlela enamandla yokulinganisa isidingo sokusetshenziswa kwedatha nempoqo yokuphepha kwedatha nobumfihlo. Ngokukhetha ngokucophelela nokusebenzisa amasu afanele, izinhlangano zingavula inani ledatha yazo kuyilapho zinciphisa ubungozi futhi zakha ukwethembana kumakhasimende azo.
Umusho wokuzihlangula: Lokhu okuthunyelwe kwebhulogi okwezinjongo zokwaziswa kuphela futhi akufanele kuthathwe njengeseluleko sezomthetho noma sezezimali. Imibono nemibono evezwe kulesi sihloko ngeyombhali futhi ayibonisi inqubomgomo esemthethweni noma isikhundla 1 sanoma iyiphi enye i-ejensi, inhlangano, umqashi, noma inkampani. Umbhali we-2 unolwazi emkhakheni wesayensi yedatha futhi unokuqonda okujulile kwamandla we I-Data Tokenization Vs Masking igxile ekuthuthukisweni nasekusetshenzisweni kobuchwepheshe be-hypercomputing. Umbhali unamalungelo obunikazi amabili e-RAG ku-AI futhi uneziqu zeComputer Science azithola eMichigan State University.
