Iyo Yakanakisa Dhata Anonymization Zvishandiso Zvekuvanzika Dziviriro Kutevedzera
Masangano anoshandisa data anonymization maturusi kubvisa ruzivo rwemunhu wega kubva kumadatasets avo. Kusateerera kunogona kutungamira kumari dzefaindi kubva kumitumbi inodzora uye kuputsa kwedata. Pasina data isingazivikanwe, haugone kushandisa kana kugovera dataset zvizere.
zhinji anonymization zvishandiso haigoni kuvimbisa kutevedza kuzere. Nzira dzekare-gen dzinogona kusiya ruzivo rwemunhu panjodzi yekusazivikanwa nevatambi vane hutsinye. Vamwe statistical anonymization nzira kuderedza mhando yedataset kusvika painonge isina kuvimbika data analytics.
We at Syntho ichakuzivisa nzira dzekusazivikanwa uye misiyano yakakosha pakati pekare-gen uye inotevera-gen zvishandiso. Isu tichakuudza nezve akanakisa data anonymization maturusi uye tipe mazano akakosha ekusarudza iwo.
Zviri Mukati
- Chii chinonzi synthetic data
- Chinoshanda sei
- Sei masangano achiishandisa
- Nzira yekutanga sei
Chii chinonzi data anonymization zvishandiso?
Data anonymization ndiyo nzira yekubvisa kana kushandura ruzivo rwakavanzika mumaseti. Masangano haakwanise kuwana, kugovana, uye kushandisa dhata rinowanikwa rinogona kurondwa zvakananga kana zvisina kunanga kune vanhu.
- General Data Protection Regulation (GDPR). Mutemo weEU inochengetedza kuvanzika kwedata rako, ichiraira mvumo yekugadzirisa data uye kupa vanhu kodzero yekuwana data. IUnited Kingdom ine mutemo wakafanana unonzi UK-GDPR.
- California Consumer Privacy Act (CCPA). Mutemo wekuCalifornian zvakavanzika inotarisa pakodzero dzevatengi maererano data sharing.
- Health Insurance Portability uye Accountability Act (HIPAA). The Privacy Rule inogadza zviyero zvekuchengetedza ruzivo rwehutano hwemurwere.
Maturusi edata anonymization anoshanda sei?
Dhata anonymization maturusi anoongorora dhataseti yeruzivo rwakadzama uye wovatsiva nedata rekunyepedzera. Iyo software inowana data rakadaro mumatafura nemakoramu, mameseji mafaera, uye magwaro akaongororwa.
Iyi nzira inobvisa data yezvinhu zvinogona kuibatanidza nevanhu kana masangano. Mhando dze data dzakavharwa nemidziyo iyi dzinosanganisira:
- Ruzivo rwunozivikanwa nemunhu (PII): Mazita, nhamba dzezvitupa, mazuva ekuzvarwa, mashoko ekubhadhara, nhamba dzenhare, uye kero yeemail.
- Ruzivo rwehutano hwakachengetedzwa (PHI): Inovhara marekodhi ekurapa, ruzivo rweinishuwarenzi yehutano, uye data yehutano hwemunhu.
- Ruzivo rwezvemari: Nhamba dzekadhi rechikwereti, nhoroondo yeakaundi yebhangi, data rekudyara, uye zvimwe zvinogona kubatana nemakambani emakambani.
Semuenzaniso, masangano ezvehutano haazivise mazita ekero dzevarwere uye ruzivo rwekufonera kuti ive nechokwadi chekutevedzera HIPAA mukutsvagisa cancer. Kambani yezvemari yakavharidzira misi yekutengeserana uye nzvimbo mumaseti avo kuti vatevedze mitemo yeGDPR.
Kunyange iyo pfungwa yakafanana, nzira dzakasiyana dzakasiyana dziripo dze data isingazivikanwe.
Data anonymization maitiro
Kusazivikanwa kunoitika munzira dzakawanda, uye hadzisi dzese nzira dzakaenzana dzakavimbika pakutevedzera uye kushandiswa. Ichi chikamu chinotsanangura musiyano pakati pemhando dzakasiyana dzenzira.
Pseudonymization
Pseudonymization inzira inodzokororwa yekubvisa zviziviso apo zviziviso zvemunhu zvinotsiviwa nemazita emanyepo. Iyo inochengetedza mepu pakati peiyo yekutanga data neiyo yakashandurwa, ine tafura yemepu yakachengetwa yakaparadzana.
The downside of pseudonymizing is that it is reversible. Neruzivo rwakawedzerwa, vanoita zvakaipa vanogona kuitsvaga vachidzokera kumunhu. Pasi pemitemo yeGDPR, pseudonymized data haitariswe isingazivikanwe data. Inoramba iri pasi pemitemo yekudzivirira data.
Data masking
Iyo data masking nzira inogadzira yakarongeka yakafanana asi yenhema vhezheni yedata ravo kuchengetedza ruzivo rwakadzama. Iyi tekinoroji inotsiva chaiyo data neakachinjika mavara, ichichengeta iyo yakafanana fomati yenguva dzose kushandiswa. Muchirevo, izvi zvinobatsira kuchengetedza kushanda kwe datasets.
Mukuita, masking data kazhinji inoderedza data utility. Inogona kutadza kuchengetedza data yepakutangakugovera kana maitiro, zvichiita kuti isanyanya kubatsira pakuongorora. Rimwe dambudziko nderekusarudza zvekufuka. Kana zvikaitwa zvisizvo, data yakavharwa inogona kuramba ichizivikanwa zvakare.
Generalization (aggregation)
Generalization inozivisa data nekuita kuti ive shoma. Inounganidza data rakafanana pamwe chete uye inoderedza kunaka kwayo, zvichiita kuti zvinyanye kutaurira zvidimbu zve data zvakasiyana. Iyi nzira inowanzo sanganisira nzira dzekupfupisa data senge avhareji kana kuenzanisa kuchengetedza mapoinzi edata.
Kupfuura-generalization kunogona kuita kuti data ive isina basa, nepo under-generalization inogona kusapa kuvanzika kwakakwana. Kune zvakare njodzi yekusasirwa kuburitswa, sezvo akaunganidzwa dhataseti angangoramba achipa yakakwana ruzivo rwekubvisa-kuzivikanwa kana asanganiswa nemamwe. dhata zvinyorwa.
Kuvhiringidza
Perturbation inogadziridza iwo ekutanga dhataseti nekutenderedza kukosha uye kuwedzera isina ruzha ruzha. Iwo mapoinzi edata anochinjwa zvinyoro-nyoro, zvichikanganisa mamiriro avo epakutanga uku vachichengetedza yakazara data mapatani.
Iyo yakaderera yekuvhiringidza ndeyekuti data haina kunyatsozivikanwa. Kana shanduko dzisina kukwana, pane njodzi yekuti maitiro ekutanga anogona kuzivikanwa zvakare.
Data swapping
Swapping inzira iyo hunhu hwehunhu huri mudataset hunorongwa patsva. Iyi nzira inonyanya nyore kushandisa. Maseti ekupedzisira haaenderane nemarekodhi ekutanga uye haaonekwe zvakananga kune awo ekutanga masosi.
Zvisina kunanga, zvisinei, iyo datasets inoramba ichidzokororwa. Yakachinja data iri panjodzi yekuburitswa kunyangwe iine mashoma echipiri masosi. Kunze kwezvo, zvakaoma kuchengetedza kutendeseka kwesemantic yeimwe data yakachinja. Semuenzaniso, kana uchitsiva mazita mudhatabhesi, iyo system inogona kutadza kusiyanisa pakati pemazita echirume neechikadzi.
Chiratidzo
Tokenization inotsiva data data zvinhu zvine tokens - zvisinganzwisisike zvakaenzana pasina hunhu hunoshandiswa. Iyo tokenized ruzivo kazhinji ingori tambo yenhamba uye mavara. Iyi tekinoroji inowanzo shandiswa kuchengetedza ruzivo rwemari uku ichichengetedza maitiro ayo anoshanda.
Imwe software inoita kuti zvinyanye kunetsa kubata uye kuyera tokeni vaults. Iyi sisitimu zvakare inouyisa njodzi yekuchengetedza: data rakajeka rinogona kunge riri panjodzi kana munhu anorwisa akapinda nepaiyo encryption vault.
Randomization
Randomization inoshandura hunhu neyakangoitika uye yekunyomba data. Iyo inzira yakatwasuka inobatsira kuchengetedza kuvanzika kwemunhu ega data.
Iyi nzira haishande kana iwe uchida kuchengetedza iyo chaiyo nhamba yekugovera. Inovimbiswa kukanganisa data rinoshandiswa kune akaomarara dataset, senge geospatial kana data yenguva. Zvisina kukwana kana zvisina kufanira kushandiswa randomisation nzira haigone kuve nechokwadi chekuchengetedzwa kwekuvanzika, kana.
Data redaction
Kudzokororwa kwedata inzira yekubvisa zvachose ruzivo kubva kumaseti: kudzima, kusavhara, kana kudzima zvinyorwa nemifananidzo. Izvi zvinodzivirira kupinda kune sensitive dhizaini yekugadzira uye inowanzoitika mumagwaro epamutemo uye epamutemo. Zviripachena kuti zvinoita kuti data risakodzere ongororo yenhamba, kudzidza modhi, uye tsvakiridzo yekiriniki.
Sezviri pachena, unyanzvi uhwu hune zvikanganiso zvinosiya maburi ayo vatambi vane hutsinye vanogona kushandisa zvisina kunaka. Vanowanzo bvisa zvinhu zvakakosha kubva kumaseti e data, izvo zvinomisa kushandiswa kwavo. Izvi hazvisizvo nezvekupedzisira-gen maitiro.
Zvishandiso zvechizvarwa chinotevera anonymization
Mazuva ano anonymization software inoshandisa hunyanzvi hwekuita kusaziva njodzi yekuzivikanwazve. Ivo vanopa nzira dzekutevedzera ese ekuvanzika mirau uchichengetedza iyo yemhando yedata.
Synthetic data kugadzira
Synthetic data yekugadzira inopa yakangwara nzira yekusaziva zita uchichengetedza data utility. Iyi nzira inoshandisa algorithms kugadzira dhataseti nyowani dzinoratidzira chaiyo data chimiro uye zvivakwa.
Synthetic data inotsiva PII nePHI nedata rekuseka risingakwanise kutsvakwa kune vanhu. Izvi zvinogonesa kutevedzwa nemitemo yekuvanzika kwedata, senge GDPR neHIPAA. Nekutora maturusi ekugadzira data ekugadzira, masangano anovimbisa kuvanzika kwedata, kuderedza njodzi dzekutyorwa kwedata, uye nekukurumidzira kuvandudzwa kwedatha-inofambiswa nedatha.
Homomorphic encryption
Homomorphic encryption (inoshandura se "chimiro chakafanana") inoshandura data mu ciphertext. Iwo akavharidzirwa dhatabheti anochengeta chimiro chakafanana neyekutanga data, zvichikonzera kurongeka kwakanakisa kwekuyedzwa.
Iyi nzira inobvumira kuita computations yakaoma zvakananga pane encrypted data pasina kuda kuibvisa kutanga. Masangano anogona kuchengetedza zvakachengeteka mafaira akavharidzirwa mugore reruzhinji uye kunze kwekugadzirisa data kune vechitatu mapato pasina kukanganisa kuchengetedza. Iyi data inopindirana, sezvo mitemo yekuvanzika isingashande kune yakavharidzirwa ruzivo.
Nekudaro, yakaoma algorithms inoda hunyanzvi hwekuita nemazvo. Kunze kwezvo, homomorphic encryption inononoka pane mashandiro pane zvisina kuvharwa data. Inogona kunge isiri iyo yakakwana mhinduro yezvikwata zveDevOps uye Quality Assurance (QA), zvinoda kukurumidza kuwana data rekuedzwa.
Chengetedza multiparty computation
Chengetedza multiparty computation (SMPC) inzira ye cryptographic yekugadzira dhatabheti nekubatana kwenhengo dzakati wandei. Bato rega rega rinonyora zvarinoisa, rinoita computations, uye rinogadziriswa data. Nenzira iyi, nhengo yega yega inowana mhedzisiro yavanoda ichichengeta yavo yega data chakavanzika.
Iyi nzira inoda mapato akawanda kuti abvise dhatabheti dzakagadzirwa, izvo zvinoita kuti ive yakavanzika. Nekudaro, iyo SMPC inoda yakakosha nguva yekuburitsa mhedzisiro.
| Yakapfuura-chizvarwa data anonymization maitiro | Zvishandiso zvechizvarwa chinotevera anonymization | ||||
|---|---|---|---|---|---|
| Pseudonymization | Inotsiva zviziviso zvemunhu nemazita emanyepo uku uchichengeta tafura yemepu yakaparadzana. | - HR data manejimendi -Kudyidzana kwemutengi rutsigiro - Ongororo dzekutsvakurudza | Synthetic data kugadzira | Inoshandisa algorithm kugadzira dhataseti nyowani dzinoratidza chimiro che data chaiyo uku uchivimbisa kuvanzika uye kutevedza. | -Data-inotungamirwa application kuvandudza - Kutsvakurudza kwekliniki - Advanced modelling - Mutengi kutengesa |
| Data masking | Inoshandura data chaiyo ine mavara emanyepo, ichichengeta yakafanana fomati. | - Mharidzo yezvemari -Mamiriro ekudzidziswa kwemushandisi | Homomorphic encryption | Shandura data kuita ciphertext uchichengeta chimiro chepakutanga, ichibvumira komputa pane yakavharidzirwa data pasina decryption. | - Chengetedza data kugadzirisa - Data computation outsourcing - Yepamusoro data kuongororwa |
| Generalization (aggregation) | Inoderedza ruzivo rwe data, kuunganidza data rakafanana. | - Demographic zvidzidzo - Zvidzidzo zvemusika | Chengetedza multiparty computation | Cryptographic nzira uko mapato akawanda anovharidzira mapindiro awo, kuita computations, uye kuwana zvakabatana zvabuda. | - Kubatana kwe data kuongororwa - Yakavanzika data kubatanidza |
| Kuvhiringidza | Inogadzirisa dataset nekutenderedza kukosha uye nekuwedzera ruzha rusina kurongeka. | - Economic data analysis - Tsvakurudzo yetraffic pateni - Sales data analysis | |||
| Data swapping | Anorongazve hunhu hwedataset kudzivirira kurongeka kwakananga. | - Zvidzidzo zvekufambisa - Yedzidzo data kuongororwa | |||
| Chiratidzo | Inotsiva data rakadzama neasina-sensitive tokens. | - Kubhadhara kugadzirisa - Tsvakurudzo yehukama hwevatengi | |||
| Randomization | Inowedzera zvisina kurongeka kana kunyomba data kuti uchinje kukosha. | - Geospatial data kuongororwa - Zvidzidzo zvemaitiro | |||
| Data redaction | Inobvisa ruzivo kubva mumaseti, | - Kugadziriswa kwegwaro remutemo -Marekodhi manejimendi | |||
Tafura 1. Kuenzanisa pakati Pekare- uye inotevera-chizvarwa anonymization maitiro
Smart data de-identification senzira nyowani yekusazivikanwa kwedata
Smart de-identification anonymize data uchishandisa AI-yakagadzirwa synthetic mock data. Mapuratifomu ane maficha anoshandura ruzivo rwakadzama kuita rinoenderana, risingazivikanwe data nenzira dzinotevera:
- De-identification software inoongorora aripo datasets uye inoratidza PII nePHI.
- Masangano anogona kusarudza kuti ndeupi data rinonzwisiswa rekutsiva neruzivo rwekunyepedzera.
- Chishandiso chinoburitsa datasets nyowani ine data rinoenderana.
Iyi tekinoroji inobatsira kana masangano achida kubatana uye kuchinjanisa data rakakosha zvakachengeteka. Izvo zvinobatsirawo kana data ichida kuitwa kuti ienderane mune akati wandei relational databases.
Smart de-identification inochengeta hukama mukati me data hwakasimba kuburikidza nemepu inofanana. Makambani anogona kushandisa iyo inogadzirwa data kune yakadzama bhizinesi analytics, muchina kudzidza kudzidziswa, uye bvunzo dzekiriniki.
Nenzira dzakawanda, iwe unoda nzira yekuona kana iyo anonymization chishandiso chakakodzera iwe.
Maitiro ekusarudza chaiyo data anonymization chishandiso
- Operational scalability. Sarudza chishandiso chinokwanisa kukwidza kumusoro nekudzika zvinoenderana nezvido zvako zvekushandisa. Tora nguva yekusimbisa kuyedza mashandiro ekushanda pasi pekuwedzera kwemabasa.
- Kubatanidzwa. Dhata anonymization maturusi anofanirwa kunyatsobatana neako aripo masisitimu uye yekuongorora software, pamwe nekuenderera mberi kwekubatanidza uye kuenderera mberi kwekutumira (CI/CD) pombi. Kuenderana nekuchengetedza kwako data, encryption, uye mapuratifomu ekugadzirisa kwakakosha pakuita zvisina musono.
- Kuenderana data mepu. Ita shuwa kuti vanochengetedza data vasingazivikanwe vane kutendeseka uye kurongeka kwenhamba inokodzera zvaunoda. Yakapfuura-chizvarwa anonymization matekiniki anodzima zvinhu zvakakosha kubva mumaseti. Zvishandiso zvemazuva ano, zvisinei, zvinochengetedza kutendeseka, zvichiita kuti data rive rakakwana kumakesi ekushandisa epamusoro.
- Nzira dzekuchengetedza. Isa pamberi maturusi anodzivirira chaiwo dataset uye zvisingazivikanwe mhedzisiro kubva mukati nekunze kutyisidzira. Iyo software inofanirwa kuiswa mune yakachengeteka mutengi zvivakwa, basa-rinoenderana nekuwana zvinodzora, uye maviri-chinhu chechokwadi APIs.
- Zvinoenderana nezvivako. Ita shuwa kuti chishandiso chinochengeta dhatabhesi munzvimbo yakachengeteka inoenderana neGDPR, HIPAA, uye CCPA mirau. Uye zvakare, inofanirwa kutsigira data backup uye maturusi ekudzoreredza kudzivirira mukana wekudzikira nekuda kwezvikanganiso zvisingatarisirwe.
- Kubhadhara muenzaniso. Funga nezve nekukurumidza uye kwenguva refu mutengo kuti unzwisise kana chishandiso ichienderana nebhajeti yako. Mamwe maturusi akagadzirirwa mabhizinesi akakura uye mabhizinesi epakati nepakati, nepo mamwe aine mamodhi anochinjika uye zvirongwa zvekushandisa.
- Rutsigiro rwehunyanzvi. Ongorora kunaka uye kuwanikwa kwevatengi uye rutsigiro rwehunyanzvi. Mupi anogona kukubatsira kubatanidza maturusi ekusaziva zita, kudzidzisa vashandi, uye kugadzirisa nyaya dzehunyanzvi.
Iwo 7 akanakisa data anonymization maturusi
Zvino zvawave kuziva zvekutsvagisa, ngationgororei zvatinotenda kuti ndiwo maturusi akavimbika masiki ruzivo rwakadzama.
1. Syntho
Syntho inopihwa simba neiyo synthetic data yekugadzira software iyo inopa mikana yekuchenjera de-identification. Iyo yepuratifomu-yakavakirwa-yakavakirwa data kugadzirwa kunounza mashandiro, zvichiita kuti masangano agadzire data zvinoenderana nezvavanoda.
An AI-powered scanner inozivisa ese PII nePHI pamadatasets, masisitimu, uye mapuratifomu. Masangano anogona kusarudza kuti ndeipi data yekubvisa kana kunyomba kuti ienderane nemirairo. Zvichakadaro, iyo subsetting ficha inobatsira kugadzira madiki madheti ekuyedzwa, kuderedza mutoro wekuchengetedza uye kugadzirisa zviwanikwa.
Iyi puratifomu inobatsira muzvikamu zvakasiyana, kusanganisira hutano, manejimendi ekutengesa, uye mari. Masangano anoshandisa iyo Syntho chikuva kugadzira isiri-kugadzira uye kugadzira tsika dzekuyedza mamiriro.
Unogona kudzidza zvakawanda nezve kugona kwaSyntho kubudikidza kuronga demo.
2. K2view
3. Broadcom
4. Kazhinji AI
5. ARX
6. Amnesia
7. Tonic.ai
Data anonymization maturusi anoshandisa makesi
Makambani mune zvemari, hutano, kushambadza, uye masevhisi eruzhinji anoshandisa maturusi ekusazivikanwa kuti arambe achitevedzera mitemo yekuvanzika kwedata. Iwo de-kuzivikanwa datasets anoshandiswa kune akasiyana mamiriro.
Software kuvandudza uye kuyedza
Maturusi ekusaziva anogonesa mainjiniya esoftware, vanoedza, uye nyanzvi dzeQA kushanda nemaseti echokwadi pasina kufumura PII. Maturusi epamberi anobatsira zvikwata kuzvipa iyo data inodiwa inoteedzera mamiriro ekuyedza epasirese pasina nyaya dzekutevedzera. Izvi zvinobatsira masangano kuvandudza mashandiro esoftware uye kunaka kwesoftware.
Mhosva chaidzo:
- Syntho's software yakagadzira isingazivikanwe bvunzo data iyo inochengetedza huwandu hwehuwandu hwe data chaiyo, zvichiita kuti vagadziri vaedze zvakasiyana-siyana pamwero mukuru.
- Google's BigQuery warehouse inopa dataset kusazivikanwa kubatsira masangano kugovera data nevatengesi pasina kutyora mitemo yekuvanzika.
Kuongorora kwechipatara
Vatsvagiri vezvokurapa, kunyanya muindasitiri yemishonga, havazivise data kuitira kuchengetedza zvakavanzika zvezvidzidzo zvavo. Vatsvagiri vanogona kuongorora mafambiro, huwandu hwevarwere, uye mhedzisiro yekurapa, zvichibatsira mukufambira mberi kwekurapa pasina kuisa njodzi kuvanzika kwemurwere.
Mhosva chaidzo:
- Erasmus Medical Center inoshandisa Syntho's anonymized AI-chizvarwa maturusi kugadzira uye kugovera datasets emhando yepamusoro yekutsvagisa zvekurapa.
Kubiridzira
Mukudzivirira hutsotsi, maturusi ekusazivikanwa anobvumira kuongororwa kwakachengeteka kwedata rekutengesa, kuona maitiro akaipa. De-identification maturusi zvakare anobvumira kudzidzisa iyo AI software pane chaiyo data kuvandudza hutsotsi uye kuona njodzi.
Mhosva chaidzo:
- Brighterion akadzidziswa pane Mastercard's anonymized transaction data kupfumisa ayo AI modhi, kunatsiridza hutsotsi hwekuona mwero uku ichideredza manyepo.
Mutengi kutengesa
Data anonymization maitiro anobatsira kuongorora zvinodiwa nevatengi. Masangano anogovana de-kuzivikanwa maitiro edhata nevanodyidzana navo vebhizinesi kunatsiridza nzira dzekushambadzira dzakanangwa uye kugadzirisa mushandisi ruzivo.
Mhosva chaidzo:
- Syntho's data anonymization chikuva chakanyatsofanotaura mutengi churn uchishandisa synthetic data inogadzirwa kubva kune dataset yevatengi vanopfuura 56,000 vane 128 columns.
Kutsikiswa kwemashoko eruzhinji
MaAgency nemasangano ehurumende anoshandisa data anonymization kugovera uye kugadzirisa ruzivo rweveruzhinji zviri pachena kune akasiyana siyana zvirongwa zveveruzhinji. Zvinosanganisira fungidziro dzeutsotsi zvichienderana nedata kubva pasocial network uye marekodhi emhosva, kuronga kwemadhorobha zvichienderana nehuwandu hwevanhu uye nzira dzezvifambiso zveveruzhinji, kana hutano hwehutano munzvimbo dzese zvichienderana nemaitiro ezvirwere.
Mhosva chaidzo:
- Indiana University yakashandisa isingazivikanwe smartphone data kubva kunosvika zviuru gumi zvemapurisa mumaguta makumi maviri nerimwe eUS kuratidza kusawirirana kwevavakidzani patrol zvichienderana nemagariro nehupfumi zvinhu.
Iyi ingori mienzaniso mishoma yatinosarudza. The anonymization software inoshandiswa kune ese maindasitiri senzira yekushandisa zvakanyanya data iripo.
Sarudza akanakisa data anonymization maturusi
Makambani ese anoshandisa database anonymization software kutevedzera mitemo yekuvanzika. Kana yakabviswa kubva kuruzivo rwemunhu, datasets inogona kushandiswa nekugovaniswa pasina njodzi dzefaindi kana maitiro ehurumende.
Nzira dzechinyakare dzekusazivikanwa dzakaita sekuchinjanisa data, masking, uye redaction haina kuchengetedzeka zvakakwana. Data de-identification inoramba iri mukana, izvo zvinoita kuti zvisaenderana kana kuti zvine njodzi. Mukuwedzera, yapfuura-gen anonymizer software kazhinji inoderedza kunaka kwedata, kunyanya mu dhata rakakura. Masangano haagone kuvimba nedata rakadaro kune analytics yepamusoro.
Iwe unofanirwa kusarudza iyo yakanakisa data anonymization software. Mabhizinesi mazhinji anosarudza iyo Syntho chikuva cheiyo yepamusoro-giredhi PII kuzivikanwa, masking, uye ekugadzira data kugadzira dhata.
Unofarira kudzidza zvakawanda here? Inzwa wakasununguka kuongorora zvinyorwa zvedu zvechigadzirwa kana taura nesu kuti tiratidze.
Nezvomunyori
Bhizinesi Rekuvandudza Bhizinesi
Uliana Krainska, Mukuru weBusiness Development kuSyntho, ane ruzivo rwepasi rose mukuvandudza software uye indasitiri yeSaaS, ane dhigirii redhigirii reDigital Bhizinesi uye Innovation, kubva kuVU Amsterdam.
Kwemakore mashanu apfuura, Uliana akaratidza kuzvipira kwakasimba mukuongorora kugona kweAI uye nekupa zano remabhizinesi ezano rekuita chirongwa cheAI.
Sevha yako synthetic data gwara izvozvi!
- Chii chinonzi synthetic data?
- Sei masangano achiishandisa?
- Kukosha kuwedzera synthetic data client makesi
- Kutanga
