Zero-Knowledge Proof Intelligent Recommendation System to Protect Students’ Data Privacy in the Digital Age
The rapid digital revolution in recent decades has resulted in an overwhelming amount of information, particularly in the realm of modern education systems and related materials. This phenomenon, often referred to as information overload, necessitates the development of educational systems that can...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2023.2222495 |
| Summary: | The rapid digital revolution in recent decades has resulted in an overwhelming amount of information, particularly in the realm of modern education systems and related materials. This phenomenon, often referred to as information overload, necessitates the development of educational systems that can effectively search, classify, and categorize this vast amount of available information. Of utmost importance for such educational information systems is the safeguarding of personal data, which refers to information that can identify an individual or their family. School records, for example, contain various types of personal data such as the individual’s name, address, contact details, disciplinary history, as well as their grades and progress checks. Even if individuals choose to make this data public, it remains inherently personal. Another category of data involves more sensitive topics such as student biometrics (e.g. fingerprints, photographs), religious beliefs, health information (e.g. allergies), or dietary restrictions, which may imply religious or health-related aspects. Processing data in this category can pose risks to individuals; hence, strict rules and appropriate consent are necessary to ensure their protection. To address these challenges, this research paper proposes a zero-knowledge proof intelligent recommendation system designed to protect students’ data privacy in the digital age. The proposed method incorporates an Intelligent Recommendation System (IRS) that utilizes an optimized version of the Matrix Factorization technique, calculated as an Eulerian Walk chart. Furthermore, the Schnorr Zero-Knowledge Proof format, based on the discrete logarithm problem, ensures the privacy of personal data during message exchange between educational entities. |
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| ISSN: | 0883-9514 1087-6545 |