1. Pfungwa yeData Masking
Data masking inozivikanwawo se data masking. Iyo inzira yehunyanzvi yekushandura, kugadzirisa kana kuvhara data rakadzama senge nhare mbozha, nhamba yekadhi rebhangi uye rumwe ruzivo kana tapa masking mitemo nemitemo. Iyi tekinoroji inonyanya kushandiswa kudzivirira data rakadzama kubva kushandiswa zvakananga munzvimbo dzisina kuvimbika.
Dhata Masking musimboti: Masking data anofanirwa kuchengetedza iwo ekutanga data maitiro, mitemo yebhizinesi, uye kukosha kwedata kuti ive nechokwadi chekuti budiriro inotevera, kuyedzwa, uye kuongororwa kwedata hakuzokanganiswe nekuvharisa. Ita shuwa kuenderana kwedata uye kuve kwechokwadi pamberi uye mushure me masking.
2. Data Masking classification
Data masking inogona kukamurwa kuita static data masking (SDM) uye ine simba data masking (DDM).
Static data masking (SDM): Static data masking inoda kugadzwa kweiyo isiri-yekugadzira nharaunda dhatabhesi yekuzviparadzanisa kubva kunharaunda yekugadzira. Sensitive data inotorwa kubva mudura rekugadzira uye yozochengetwa mune isiri-yekugadzira dhatabhesi. Nenzira iyi, iyo deensitized data yakaparadzaniswa kubva munzvimbo yekugadzira, iyo inosangana nezvinodiwa zvebhizinesi uye inova nechokwadi chekuchengetedza data rekugadzira.
Dynamic Data masking (DDM): Inowanzo shandiswa munzvimbo yekugadzira kuita desensitize data yakavanzika munguva chaiyo. Dzimwe nguva, mazinga akasiyana ekuisa masking anodiwa kuti uverenge yakafanana inonzwisa data mumamiriro akasiyana. Semuyenzaniso, mabasa akasiyana uye mvumo anogona kuita akasiyana masking zvirongwa.
Kuzivisa data uye zvigadzirwa zve data masking application
Mamiriro akadai anonyanya kubatanidza zvigadzirwa zvemukati zvekutarisisa data kana bhodhi, zvigadzirwa zve data rebasa rekunze, uye mishumo yakavakirwa pakuongorora kwedata, senge mishumo yebhizinesi uye ongororo yeprojekiti.
3. Data Masking Solution
Zvakajairwa data masking zvirongwa zvinosanganisira: kusashanda, kusarudzika kukosha, kutsiva data, symmetric encryption, kukosha kwepakati, kumisa uye kutenderera, nezvimwe.
Kusashanda: Kusashanda kunoreva encryption, truncation, kana kuhwanda kwe data rakavanzika. Ichi chirongwa chinowanzo kutsiva chaiyo data nezviratidzo zvakakosha (senge *). Iko kushanda kuri nyore, asi vashandisi havagone kuziva chimiro cheiyo data yekutanga, iyo inogona kukanganisa inotevera data application.
Random Value: Ukoshi husina kurongeka hunoreva kutsiva zvisina tsarukano data (nhamba dzinotsiva manhamba, mavara anotsiva mavara, uye mavara anotsiva mavara). Iyi masking nzira inovimbisa iyo fomati yedata inonzwisisika kune imwe nhanho uye kufambisa inotevera data application. Kuvhara maduramazwi kungadiwa pamashoko ane revo, akadai semazita evanhu nenzvimbo.
Data Replacement: Kutsiviwa kwedata kwakafanana nekuvharika kwezvisina maturo uye zvisina kurongeka, kunze kwekuti pachinzvimbo chekushandisa mavara akakosha kana maitiro asina kujairika, iyo masking data inotsiviwa nehumwe kukosha.
Symmetric Encryption: Symmetric encryption inzira yakakosha inodzoreredza masking. Iyo encrypt data inonzwisisika kuburikidza ne encryption kiyi uye algorithms. Iyo ciphertext fomati inopindirana neyekutanga data mumitemo ine musoro.
Avhareji: Avhareji chirongwa chinowanzoshandiswa muhuwandu hwehuwandu. Nezvenhamba yedata, isu tinotanga taverenga zvirevo zvavo, tobva tagovera zvimiro zvedesensitized zvakatenderedza zvinoreva, nokudaro tichichengeta huwandu hwe data nguva dzose.
Offset uye Rounding: Iyi nzira inoshandura iyo data yedhijitari nekungochinja. Iko kutenderedza kutenderedza kunovimbisa kunenge kwechokwadi kwehuwandu uchichengetedza chengetedzo yedata, iri padyo neiyo data chaiyo kupfuura zvirongwa zvekare, uye ine kukosha kukuru mumamiriro ekuongorora kukuru kwedata.
Iyo Recommend Model "ML-NPB-5660"yeData Masking
4. Inowanzoshandiswa Data Masking Techniques
(1). Statistical Techniques
Data sampling uye data aggregation
- Sampling yedata: Kuongorora uye kuongororwa kweiyo yekutanga data yakatarwa nekusarudza mumiriri wechikamu cheiyo data seti inzira yakakosha yekuvandudza kushanda kwemaitiro e-de-identification.
-Kuunganidzwa kwedata: Semuunganidzwa wematekiniki ehuwandu (senge muchidimbu, kuverenga, kuenzana, hukuru uye hushoma) hunoshandiswa kune hunhu mumicrodata, mhedzisiro inomiririra marekodhi ese mune yekutanga data set.
(2). Cryptography
Cryptography inzira yakajairika yekudzima kana kusimudzira kushanda kwe desensitization. Mhando dzakasiyana dze encryption algorithms dzinogona kuwana akasiyana desensitization mhedzisiro.
-Deterministic encryption: Iyo isiri-yakasarudzika symmetric encryption. Iyo inowanzo gadzira ID data uye inogona kudhipfenyura uye kudzoreredza ciphertext kune yekutanga ID kana zvichidikanwa, asi kiyi inoda kuchengetedzwa nemazvo.
-Isingadzoreki encryption: Iyo hashi basa rinoshandiswa kugadzirisa data, iyo inowanzo shandiswa kune ID data. Haikwanise kucheneswa zvakananga uye hukama hwemepu hunofanirwa kuchengetedzwa. Mukuwedzera, nekuda kwechimiro chebasa rehashi, kudhumhana kwedata kunogona kuitika.
- Homomorphic encryption: Iyo ciphertext homomorphic algorithm inoshandiswa. Chimiro chayo ndechekuti mhedzisiro ye ciphertext operation yakafanana neye plaintext operation mushure me decryption. Naizvozvo, inowanzoshandiswa kugadzirisa nhamba dzeminda, asi haishandiswe zvakanyanya nekuda kwezvikonzero zvekuita.
(3). System Technology
Iyo tekinoroji yekudzvinyirira inodzima kana kuchengetedza data zvinhu izvo zvisingasangane nekuchengetedzwa kwekuvanzika, asi hazvizvishambadzire.
-Masking: inoreva nzira yakajairika yedesensitization yekuvharisa kukosha kwehunhu, senge nhamba yeanopikisa, ID kadhi yakanyorwa neasterisk, kana kero yakaderedzwa.
- Dzvinyiriro yemunharaunda: inoreva maitiro ekudzima chaiwo hunhu hutsika (makoramu), kubvisa isina-yakakosha data data;
- Rekodha kudzvinyirirwa: inoreva maitiro ekudzima marekodhi chaiwo (mitsara), kudzima zvisina kukosha data rekodhi.
(4). Pseudonym Technology
Pseudomanning idhizaini yekusaziva iyo inoshandisa pseudonym kutsiva yakananga identifier (kana chimwe chinoziva chinongedzo). Nzira dzekunyepedzera dzinogadzira zviziviso zvakasiyana zvechinyorwa chega chega cheruzivo, pachinzvimbo chezviziviso zvakanangana kana zvine hunyoro.
-Inokwanisa kugadzira zvimiro zvisina tsarukano zvakazvimiririra kuti ienderane neiyo ID yekutanga, chengetedza tafura yemepu, uye kunyatso kudzora kupinda patafura yemepu.
- Iwe unogona zvakare kushandisa encryption kugadzira pseudonyms, asi unofanirwa kuchengeta kiyi yedecryption zvakanaka;
Iyi tekinoroji inoshandiswa zvakanyanya muchiitiko chenhamba huru yevashandisi vedata vakazvimiririra, seOpenID mune yakavhurika papuratifomu mamiriro, apo vanogadzira vakasiyana vanowana maOpenids akasiyana emushandisi mumwe chete.
(5). Generalization Techniques
Generalization nzira inoreva de-chiziviso nzira iyo inoderedza granularity yeakasarudzwa hunhu museti yedata uye inopa yakawedzera kujekerwa uye isinganzwisisike tsananguro yedata. Generalization tekinoroji iri nyore kuita uye inogona kuchengetedza huchokwadi hwerekodhi-level data. Inowanzoshandiswa mune zvigadzirwa zve data kana mishumo yedata.
-Kutenderedza: kunosanganisira kusarudza hwaro hwekutenderera hwehunhu hwakasarudzwa, senge kumusoro kana kudzika forensics, kuburitsa mhedzisiro 100, 500, 1K, uye 10K.
-Nyepamusoro nepasi macoding matekiniki: Tsiva kukosha pamusoro (kana pazasi) chikumbaridzo nechikumbaridzo chinomiririra yepamusoro (kana pasi) nhanho, ichipa mhedzisiro ye "pamusoro X" kana "pazasi X"
(6). Randomization Techniques
Semhando ye-de-identification tekinoroji inoreva kugadzirisa kukosha kwehunhu kuburikidza nekusarongeka, kuitira kuti kukosha mushure mekuita randomisation kwakasiyana kubva kune yekutanga kukosha chaiko. Kuita uku kunoderedza kugona kweanorwisa kuti atore kukosha kwehunhu kubva kune humwe hunhu hwehunhu mune imwecheteyo data rekodhi, asi inokanganisa huchokwadi hwe data rinobuda, rinowanzozivikanwa nedata rebvunzo rekugadzira.
Nguva yekutumira: Sep-27-2022
