رؤى - Computer Science - # Data Sanitization Algorithms

String Sanitization Under Edit Distance: Improved and Generalized

Q: How do the proposed algorithms compare to existing methods in terms of efficiency and accuracy

The proposed algorithms in the context provided aim to solve the ETFS (Edit distance, Total order, Frequency, Sanitization) problem and its generalization AETFS (Arbitrary lengths, Edit distance, Total order, Frequency, Sanitization). These algorithms offer significant improvements in terms of efficiency and accuracy compared to existing methods. In terms of efficiency: The algorithm for solving ETFS achieves a time complexity of O(n2 log2 k), which is an improvement over previous methods. The algorithm for solving AETFS has a time complexity of O(k|I|n + Ln), showcasing enhanced efficiency in handling arbitrary length sensitive patterns. In terms of accuracy: The algorithms ensure that the constructed strings have minimal edit distance from the original string while adhering to specific constraints related to sensitive patterns. By utilizing dynamic programming tables and directed acyclic graphs efficiently, these algorithms provide accurate solutions that preserve privacy and utility effectively. Overall, these proposed algorithms demonstrate superior efficiency by optimizing computation time while maintaining high levels of accuracy in addressing string sanitization challenges under edit distance constraints.

Q: What are the potential applications of these improved techniques beyond string sanitization

Beyond string sanitization applications discussed in the context such as data privacy preservation and knowledge hiding processes, the improved techniques presented can have various potential applications: Bioinformatics: These techniques can be applied in DNA sequence analysis where preserving genetic information while ensuring privacy is crucial. Cybersecurity: Enhancements in data sanitization can strengthen cybersecurity practices by enabling secure processing and sharing of confidential information without compromising sensitive data. Machine Learning: Improved string sanitization methods can enhance data preprocessing steps before feeding information into machine learning models for better performance. Natural Language Processing: Applications involving text mining or sentiment analysis could benefit from advanced techniques that maintain data integrity during processing. Healthcare Data Management: In healthcare systems where patient records need protection against unauthorized access or disclosure, these techniques can play a vital role in ensuring confidentiality while allowing necessary analyses on sanitized datasets.

Q: How might advancements in data sanitization impact cybersecurity practices

Advancements in data sanitization have significant implications for cybersecurity practices: Enhanced Privacy Protection: Improved techniques enable organizations to sanitize sensitive information effectively before storage or transmission, reducing risks associated with data breaches or unauthorized access. Compliance with Regulations: Stringent regulations like GDPR require businesses to protect personal data through proper anonymization or pseudonymization processes facilitated by advanced sanitization methods. Secure Data Sharing: With robust string sanitization approaches, organizations can securely share datasets with external parties without divulging confidential details embedded within the information. Prevention of Insider Threats: By implementing efficient sanitation procedures on internal databases containing proprietary knowledge or intellectual property rights ensures protection against insider threats aiming at misusing critical company assets. These advancements ultimately contribute towards establishing a more resilient cybersecurity framework capable of safeguarding valuable digital assets across various industries and sectors.

المفاهيم الأساسية

The author presents improved algorithms for string sanitization under edit distance, addressing privacy concerns while maintaining data utility.

الملخص

The content discusses the ETFS problem, proposing optimal solutions and techniques for efficient computation. It also introduces algorithms to handle sensitive patterns of arbitrary lengths.

تخصيص الملخص

إعادة الكتابة بالذكاء الاصطناعي

إنشاء الاستشهادات

ترجمة المصدر

إلى لغة أخرى

إنشاء خريطة ذهنية

من محتوى المصدر

زيارة المصدر

arxiv.org

الإحصائيات

ETFS can be solved in O(n2k) time.
The AETFS problem can be solved in O(k|I|n + Ln) time.
The ETFS-DP algorithm solves the ETFS problem efficiently.

اقتباسات

الرؤى الأساسية المستخلصة من

String Sanitization Under Edit Distance

by Takuya Mieno... في arxiv.org 03-12-2024

https://arxiv.org/pdf/2007.08179.pdf

استفسارات أعمق

How do the proposed algorithms compare to existing methods in terms of efficiency and accuracy

The proposed algorithms in the context provided aim to solve the ETFS (Edit distance, Total order, Frequency, Sanitization) problem and its generalization AETFS (Arbitrary lengths, Edit distance, Total order, Frequency, Sanitization). These algorithms offer significant improvements in terms of efficiency and accuracy compared to existing methods.
In terms of efficiency:

The algorithm for solving ETFS achieves a time complexity of O(n2 log2 k), which is an improvement over previous methods.
The algorithm for solving AETFS has a time complexity of O(k|I|n + Ln), showcasing enhanced efficiency in handling arbitrary length sensitive patterns.
In terms of accuracy:

The algorithms ensure that the constructed strings have minimal edit distance from the original string while adhering to specific constraints related to sensitive patterns.
By utilizing dynamic programming tables and directed acyclic graphs efficiently, these algorithms provide accurate solutions that preserve privacy and utility effectively.
Overall, these proposed algorithms demonstrate superior efficiency by optimizing computation time while maintaining high levels of accuracy in addressing string sanitization challenges under edit distance constraints.

What are the potential applications of these improved techniques beyond string sanitization

Beyond string sanitization applications discussed in the context such as data privacy preservation and knowledge hiding processes, the improved techniques presented can have various potential applications:

Bioinformatics: These techniques can be applied in DNA sequence analysis where preserving genetic information while ensuring privacy is crucial.

Cybersecurity: Enhancements in data sanitization can strengthen cybersecurity practices by enabling secure processing and sharing of confidential information without compromising sensitive data.

Machine Learning: Improved string sanitization methods can enhance data preprocessing steps before feeding information into machine learning models for better performance.

Natural Language Processing: Applications involving text mining or sentiment analysis could benefit from advanced techniques that maintain data integrity during processing.

Healthcare Data Management: In healthcare systems where patient records need protection against unauthorized access or disclosure, these techniques can play a vital role in ensuring confidentiality while allowing necessary analyses on sanitized datasets.

How might advancements in data sanitization impact cybersecurity practices

Advancements in data sanitization have significant implications for cybersecurity practices:

Enhanced Privacy Protection: Improved techniques enable organizations to sanitize sensitive information effectively before storage or transmission, reducing risks associated with data breaches or unauthorized access.

Compliance with Regulations: Stringent regulations like GDPR require businesses to protect personal data through proper anonymization or pseudonymization processes facilitated by advanced sanitization methods.

Secure Data Sharing: With robust string sanitization approaches, organizations can securely share datasets with external parties without divulging confidential details embedded within the information.

Prevention of Insider Threats: By implementing efficient sanitation procedures on internal databases containing proprietary knowledge or intellectual property rights ensures protection against insider threats aiming at misusing critical company assets.

These advancements ultimately contribute towards establishing a more resilient cybersecurity framework capable of safeguarding valuable digital assets across various industries and sectors.