TableGuard -- Securing Structured & Unstructured Data
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917870288502784 |
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| author | Sharma, Anantha Deshmukh, Ajinkya |
| author_facet | Sharma, Anantha Deshmukh, Ajinkya |
| contents | With the increasing demand for data sharing across platforms and organizations, ensuring the privacy and security of sensitive information has become a critical challenge. This paper introduces "TableGuard". An innovative approach to data obfuscation tailored for relational databases. Building on the principles and techniques developed in prior work on context-sensitive obfuscation, TableGuard applies these methods to ensure that API calls return only obfuscated data, thereby safeguarding privacy when sharing data with third parties. TableGuard leverages advanced context-sensitive obfuscation techniques to replace sensitive data elements with contextually appropriate alternatives. By maintaining the relational integrity and coherence of the data, our approach mitigates the risks of cognitive dissonance and data leakage. We demonstrate the implementation of TableGuard using a BERT based transformer model, which identifies and obfuscates sensitive entities within relational tables. Our evaluation shows that TableGuard effectively balances privacy protection with data utility, minimizing information loss while ensuring that the obfuscated data remains functionally useful for downstream applications. The results highlight the importance of domain-specific obfuscation strategies and the role of context length in preserving data integrity. The implications of this research are significant for organizations that need to share data securely with external parties. TableGuard offers a robust framework for implementing privacy-preserving data sharing mechanisms, thereby contributing to the broader field of data privacy and security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TableGuard -- Securing Structured & Unstructured Data Sharma, Anantha Deshmukh, Ajinkya Cryptography and Security Artificial Intelligence Computation and Language Information Retrieval Machine Learning With the increasing demand for data sharing across platforms and organizations, ensuring the privacy and security of sensitive information has become a critical challenge. This paper introduces "TableGuard". An innovative approach to data obfuscation tailored for relational databases. Building on the principles and techniques developed in prior work on context-sensitive obfuscation, TableGuard applies these methods to ensure that API calls return only obfuscated data, thereby safeguarding privacy when sharing data with third parties. TableGuard leverages advanced context-sensitive obfuscation techniques to replace sensitive data elements with contextually appropriate alternatives. By maintaining the relational integrity and coherence of the data, our approach mitigates the risks of cognitive dissonance and data leakage. We demonstrate the implementation of TableGuard using a BERT based transformer model, which identifies and obfuscates sensitive entities within relational tables. Our evaluation shows that TableGuard effectively balances privacy protection with data utility, minimizing information loss while ensuring that the obfuscated data remains functionally useful for downstream applications. The results highlight the importance of domain-specific obfuscation strategies and the role of context length in preserving data integrity. The implications of this research are significant for organizations that need to share data securely with external parties. TableGuard offers a robust framework for implementing privacy-preserving data sharing mechanisms, thereby contributing to the broader field of data privacy and security. |
| title | TableGuard -- Securing Structured & Unstructured Data |
| topic | Cryptography and Security Artificial Intelligence Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2408.07045 |