{"id":1449,"date":"2026-06-01T04:47:49","date_gmt":"2026-05-31T21:47:49","guid":{"rendered":"https:\/\/vncybers.vn\/?page_id=1449"},"modified":"2026-08-04T04:51:30","modified_gmt":"2026-08-03T21:51:30","slug":"apac-ai4privacy","status":"publish","type":"page","link":"https:\/\/vncybers.vn\/en\/apac-ai4privacy\/","title":{"rendered":"APAC AI4Privacy"},"content":{"rendered":"
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APAC AI4Privacy<\/h1>\t\t\t\t<\/div>\n\t\t\t\t
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VNCyberS supports organizations across Asia in deploying AI4Privacy<\/h3>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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VNCyberS is proud to announce its role as Asia-Pacific Regional Partner Manager, working with Artificial Intelligence Suisse SA (AI4Privacy, Switzerland) on the launch of a 3,000,000-sample synthetic personally identifiable information (PII) dataset, deeply optimized for eight major Asian languages: Chinese, Korean, Indonesian, Malay, Japanese, Filipino, Thai, and Vietnamese.<\/b><\/p>\n

We believe that knowledge sharing, technology transfer, and joint research with global partners are key to addressing increasingly complex cross-border cybersecurity challenges.<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAPAC AI4Privacy\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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1. Detect Personal Data – Ph\u00e1t hi\u1ec7n d\u1eef li\u1ec7u c\u00e1 nh\u00e2n
<\/b>\u26e8 Identify names, phone numbers, emails, addresses, identifiers, accounts, and sensitive content in multilingual data.<\/span><\/p>

2. Mask & Protect PII – Che gi\u1ea5u v\u00e0 b\u1ea3o v\u1ec7 PII
<\/strong>\u26e8 Apply masking, redaction, tokenization, and controlled reveal to reduce personal data exposure risk.<\/span><\/p>

3. Synthetic Data for Testing -D\u1eef li\u1ec7u t\u1ed5ng h\u1ee3p cho ki\u1ec3m th\u1eed
<\/strong>\u26e8 <\/span>Use synthetic data instead of real personal data for training, testing, benchmarking, and PoC activities.<\/p>

4. Compliance Readiness – S\u1eb5n s\u00e0ng tu\u00e2n th\u1ee7
<\/strong>\u26e8 <\/span>Support assessment of data protection processes against PDP 2025, GDPR, PIPL, PIPA, PDPA, and related privacy frameworks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWHY AI4PRIVACY APAC MATTERS\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Organizations across Asia process large volumes of personal data in banking, government, telecommunications, healthcare, and digital platforms. However, PII in APAC is multilingual, multi-format, heavily regulated, and difficult to use safely as real data for AI training and testing.<\/p>

\"\ud83c\udf0e\" Multilingual PII<\/strong>
\u26e8 <\/span>PII in APAC is not only English. Systems must identify names, addresses, phone numbers, emails, and sensitive content across many Asian languages.<\/p>

\"\ud83d\udcdd\" Country-Specific Formats<\/strong>
\u26e8 <\/span>PII formats in APAC differ by country, including names, addresses, phone numbers, postal codes, and synthetic identifier patterns.<\/p>

\"\ud83d\udda5\ufe0f\" Synthetic Data for Safe Testing<\/strong>
\u26e8 <\/span>Synthetic data enables training, benchmarking, PoC testing, and masking\/tokenization assessment without using real personal data.<\/p>

\"\u2705\" Enterprise Compliance Readiness<\/strong>
\u26e8 <\/span>Support assessment of personal data protection processes against PDP 2025, GDPR, PIPL, PIPA, PDPA, and other related privacy regulations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPII-MASKING-3M APAC DATASET\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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\u26e8 <\/span>This large-scale synthetic dataset supports detection, masking, tokenization, and personal data protection assessment in multilingual Asian contexts.<\/strong>
\u26e8 <\/span>The PII-Masking-3M APAC Dataset is a large-scale synthetic dataset initiative designed to help AI teams, data protection engineers, and enterprises test their ability to detect, classify, and mask personally identifiable information across multiple Asian languages.<\/strong>
\u26e8 <\/span>The dataset helps organizations evaluate workflows such as PII detection, masking, redaction, tokenization, controlled reveal, and compliance readiness without using real personal data during training, testing, or PoC stages.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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1. 3M Synthetic Samples – 3 tri\u1ec7u m\u1eabu d\u1eef li\u1ec7u t\u1ed5ng h\u1ee3p<\/strong>
\u26e8 <\/span>A large-scale synthetic data repository for training, testing, benchmarking, and evaluating AI models for personal data protection.<\/p>

2. 8 Asian Languages – 8 ng\u00f4n ng\u1eef ch\u00e2u \u00c1<\/strong>
\u26e8 <\/span>Covers key APAC language contexts, including Vietnamese, Indonesian, Malay, Filipino, Thai, Chinese, Japanese, and Korean.
3. PII Detection & Classification – Ph\u00e1t hi\u1ec7n v\u00e0 ph\u00e2n lo\u1ea1i PII
Supports recognition of data groups such as names, phone numbers, emails, addresses, synthetic identifiers, accounts, digital identifiers, and sensitive content.<\/p>

4. Masking & Tokenization Testing – Ki\u1ec3m th\u1eed masking v\u00e0 tokenization<\/strong>
\u26e8 <\/span>Enables evaluation of masking, redaction, pseudonymization, tokenization, and controlled reveal techniques in a safe environment.<\/p>

5. Entity-Level Annotation – G\u00e1n nh\u00e3n theo th\u1ef1c th\u1ec3<\/strong>
\u26e8 <\/span>Data samples can be annotated at entity and span levels to support benchmarking, NER model training, and system accuracy evaluation.<\/p>

6. Enterprise PoC Ready – S\u1eb5n s\u00e0ng cho PoC doanh nghi\u1ec7p t\u1ed5 ch\u1ee9c<\/strong>
\u26e8 <\/span>Helps shorten PoC\/Pilot preparation time for banks, government agencies, telecommunications providers, healthcare organizations, and large enterprise platforms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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The PII-Masking-3M APAC Dataset supports evaluation of privacy-preserving AI across eight Asian languages and country-specific PII patterns. It is designed to reflect privacy challenges in multilingual APAC environments, including local naming conventions, phone number formats, postal-style addresses, account data, digital identifiers, and synthetic identifier patterns.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\ud83c\uddfb\ud83c\uddf3 Vietnamese<\/strong>
Personal names, phone numbers, addresses, and synthetic identifier patterns.<\/p>

\ud83c\uddee\ud83c\udde9 Indonesian<\/strong>
Local names, phone\/address formats, and account-style data.<\/p>

\ud83c\uddf2\ud83c\uddfe Malay<\/strong>
Malay names, phone numbers, postal codes, and synthetic identifier patterns.<\/p>

\ud83c\uddf5\ud83c\udded Filipino<\/strong>
Ti\u1ebfng Filipino\u2028C\u00e1c m\u1eabu v\u0103n b\u1ea3n doanh nghi\u1ec7p b\u1eb1ng ti\u1ebfng Filipino v\u00e0 v\u0103n b\u1ea3n pha tr\u1ed9n Anh – Filipino.<\/p>

\ud83c\uddf9\ud83c\udded Thai<\/strong>
Thai script, local names, phone numbers, and address formats.<\/p>

\ud83c\udde8\ud83c\uddf3 Chinese<\/strong>
Chinese names, addresses, phone number formats, and synthetic identifier patterns.<\/p>

\ud83c\uddef\ud83c\uddf5 Japanese<\/strong>
Kanji\/Kana names, postal-style addresses, and Japan-specific PII patterns.<\/p>

\ud83c\uddf0\ud83c\uddf7 Korean<\/strong>
Korean names, phone numbers, addresses, and account-style data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDataset Pipeline\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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The PII-3M APAC Dataset is designed around a structured data pipeline that supports the full process from synthetic data generation and PII taxonomy standardization to annotation, masking\/tokenization testing, model evaluation, and enterprise PoC preparation.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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1. Synthetic Asian Text Data<\/strong>
\u26e8 <\/span>Generate multilingual synthetic data for APAC contexts.<\/p>

2. PII Entity Taxonomy<\/strong>
\u26e8 <\/span>Standardize PII entity groups and sensitive data formats.<\/p>

3. Annotation & QA<\/strong>
\u26e8 <\/span>Perform entity-level\/span-level annotation and quality assurance.<\/p>

4. Masking \/ Tokenization<\/strong>
\u26e8 <\/span>Test masking, redaction, tokenization, and controlled reveal.<\/p>

5. Evaluation & Enterprise Deployment<\/strong>
\u26e8 <\/span>Evaluate performance and prepare for PoC\/Pilot deployment.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEnterprise Use Cases\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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The PII-3M APAC Dataset helps organizations evaluate and deploy AI4Privacy across enterprise use cases, from PII detection, masking, tokenization, and AI model training to PoC\/Pilot preparation for sensitive data protection and compliance readiness assessment.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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1. Benchmark ph\u00e1t hi\u1ec7n PII<\/strong>
\u26e8 <\/span>Evaluate detection performance for names, phone numbers, email addresses, residential addresses, synthetic identifier patterns, account data, digital identifiers, and sensitive content.<\/p>

2. Ki\u1ec3m th\u1eed masking v\u00e0 tokenization d\u1eef li\u1ec7u<\/strong>
\u26e8 <\/span>Test masking, redaction, pseudonymization, tokenization, and controlled reveal workflows before deploying applications in enterprise environments.<\/p>

3. \u0110\u00e1nh gi\u00e1 s\u1eb5n s\u00e0ng tu\u00e2n th\u1ee7<\/strong>
\u26e8 <\/span>H\u1ed7 tr\u1ee3 \u0111\u00e1nh gi\u00e1 c\u00e1c quy tr\u00ecnh b\u1ea3o v\u1ec7 d\u1eef li\u1ec7u c\u00e1 nh\u00e2n theo PDP 2025, GDPR, PIPL, PIPA, PDPA v\u00e0 c\u00e1c khung b\u1ea3o v\u1ec7 quy\u1ec1n ri\u00eang t\u01b0 li\u00ean quan kh\u00e1c.<\/p>

4. Hu\u1ea5n luy\u1ec7n v\u00e0 \u0111\u00e1nh gi\u00e1 m\u00f4 h\u00ecnh AI<\/strong>
\u26e8 <\/span>Support AI\/ML teams in developing, fine-tuning, and evaluating NER models, privacy classifiers, redaction models, and hybrid private data detection systems.<\/p>

5. D\u1eef li\u1ec7u t\u1ed5ng h\u1ee3p cho ki\u1ec3m th\u1eed an to\u00e0n
<\/strong>\u26e8 <\/span>Enable safe testing in Dev, UAT, benchmark, and PoC environments without using real personal data.<\/p>

6. R\u00fat ng\u1eafn PoC \/ Pilot doanh nghi\u1ec7p<\/strong>
\u26e8 <\/span>Reduce PoC preparation time for banks, government agencies, telecommunications providers, healthcare organizations, and large enterprises.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVNCybers\u2019 Role in APAC AI4Privacy\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t
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VNCyberS supports localization, enterprise engagement, and APAC market development for AI4Privacy initiatives, including the PII-Masking-3M APAC Dataset.<\/strong><\/p>

In its role supporting APAC market development and localization in Vietnam, VNCyberS works with organizations across the region to assess personal data protection needs, define enterprise use cases, prepare PoC\/Pilot programs, and apply AI4Privacy to real-world challenges such as PII detection, data masking, tokenization, synthetic data testing, and compliance readiness.<\/p>

VNCyberS focuses on supporting organizations in sectors with high requirements for sensitive data protection, including banking, government, telecommunications, healthcare, and digital platforms, while connecting each APAC market\u2019s localization requirements to AI4Privacy development and adoption.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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1. APAC Localization Support – H\u1ed7 tr\u1ee3 b\u1ea3n \u0111\u1ecba h\u00f3a APAC<\/strong>
\u26e8 <\/span>Collect and analyze language requirements, PII formats, legal contexts, and enterprise use cases in Vietnam and Asian markets.<\/p>

2. Enterprise Use Case Advisory – T\u01b0 v\u1ea5n use case doanh nghi\u1ec7p<\/strong>
\u26e8 <\/span>H\u1ed7 tr\u1ee3 t\u1ed5 ch\u1ee9c x\u00e1c \u0111\u1ecbnh c\u00e1c b\u00e0i to\u00e1n ph\u00f9 h\u1ee3p \u0111\u1ec3 \u1ee9ng d\u1ee5ng AI4Privacy, bao g\u1ed3m ph\u00e1t hi\u1ec7n PII, masking, tokenization, ki\u1ec3m th\u1eed d\u1eef li\u1ec7u t\u1ed5ng h\u1ee3p v\u00e0 \u0111\u00e1nh gi\u00e1 s\u1eb5n s\u00e0ng tu\u00e2n th\u1ee7.<\/p>

3. PoC \/ Pilot Planning – L\u1eadp k\u1ebf ho\u1ea1ch ph\u01b0\u01a1ng \u00e1n k\u1ef9 thu\u1eadt PoC\/Pilot<\/strong>
\u26e8 <\/span>H\u1ed7 tr\u1ee3 x\u00e2y d\u1ef1ng ph\u1ea1m vi th\u1eed nghi\u1ec7m, ti\u00eau ch\u00ed \u0111\u00e1nh gi\u00e1, d\u1eef li\u1ec7u ki\u1ec3m th\u1eed t\u1ed5ng h\u1ee3p, KPI k\u1ef9 thu\u1eadt v\u00e0 l\u1ed9 tr\u00ecnh tri\u1ec3n khai PoC\/Pilot cho kh\u00e1ch h\u00e0ng doanh nghi\u1ec7p.<\/p>

4. APAC Market Engagement – K\u1ebft n\u1ed1i th\u1ecb tr\u01b0\u1eddng APAC<\/strong>
\u26e8 <\/span>H\u1ed7 tr\u1ee3 k\u1ebft n\u1ed1i v\u1edbi c\u00e1c t\u1ed5 ch\u1ee9c, \u0111\u1ed1i t\u00e1c v\u00e0 kh\u00e1ch h\u00e0ng ti\u1ec1m n\u0103ng trong khu v\u1ef1c APAC, \u0111\u1eb7c bi\u1ec7t trong c\u00e1c l\u0129nh v\u1ef1c ch\u00ednh ph\u1ee7, ng\u00e2n h\u00e0ng, vi\u1ec5n th\u00f4ng, y t\u1ebf v\u00e0 c\u00f4ng ngh\u1ec7.<\/p>


\u2197\ufe0f \u0110\u0103ng k\u00fd cu\u1ed9c h\u1eb9n cho doanh nghi\u1ec7p APAC<\/a> | \u2197\ufe0f Contact the VNCyberS APAC team<\/a> | \u2197\ufe0f Request the Dataset Card<\/a> | \u2197\ufe0f Trao \u0111\u1ed5i v\u1ec1 PoC\/Pilot<\/a><\/strong><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Our Clients<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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We are proud to stand alongside pioneering brands that trust us with the mission of protecting their data and turning their vision into reality.<\/p>

Together, we can make extraordinary things happen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Contact Us<\/h2>

Email: [email protected]
Phone: +84 903260277<\/p>\t\t<\/div>\n\t\t\t\t\t

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\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg>\t\t\n\t\t\tCONTACT\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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APAC AI4Privacy VNCybers h\u1ed7 tr\u1ee3 c\u00e1c t\u1ed5 ch\u1ee9c t\u1ea1i ch\u00e2u \u00c1 tri\u1ec3n khai AI4Privacy VNCyberS t\u1ef1 h\u00e0o th\u00f4ng b\u00e1o v\u1edbi vai tr\u00f2 l\u00e0 Qu\u1ea3n l\u00fd \u0110\u1ed1i t\u00e1c khu v\u1ef1c Ch\u00e2u \u00c1 \u2013 Th\u00e1i B\u00ecnh D\u01b0\u01a1ng, \u0111\u1ed3ng h\u00e0nh c\u00f9ng Artificial Intelligence Suisse SA (AI4Privacy \u2013 Th\u1ee5y S\u0129) trong s\u1ef1 ki\u1ec7n ra m\u1eaft b\u1ed9 d\u1eef li\u1ec7u th\u00f4ng […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-1449","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/pages\/1449","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/comments?post=1449"}],"version-history":[{"count":49,"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/pages\/1449\/revisions"}],"predecessor-version":[{"id":1851,"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/pages\/1449\/revisions\/1851"}],"wp:attachment":[{"href":"https:\/\/vncybers.vn\/en\/wp-json\/wp\/v2\/media?parent=1449"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}