PptxGenJS Presentation

[Virtual Presenter] Privacy Preserving Data Mining Techniques This presentation will explore the essential concepts and techniques of privacy preserving data mining, a field that addresses the critical challenge of mining valuable insights from data while protecting sensitive information. By M Murali.

[Virtual Presenter] Introduction to Data Mining Extracting Knowledge Pattern Discovery It involves discovering patterns, trends, and relationships hidden within the data. Data mining is the process of extracting valuable insights from large datasets. Decision Support Data mining techniques empower businesses and organizations to make informed decisions..

[Audio] Challenges in Traditional Data Mining Data Sensitivity Privacy Risks Ethical Concerns Traditional data mining often requires access to sensitive personal information. Sharing or analyzing sensitive data poses risks of privacy breaches and identity theft. Ethical considerations surrounding data usage are paramount in protecting individuals' privacy..

[Audio] Concepts of Privacy Preservation Data Anonymization Techniques that remove or obfuscate identifying information from data. Differential Privacy Adds random noise to data to prevent inference of individual information. Secure Multiparty Computation Allows multiple parties to jointly compute on data without revealing their individual inputs..

[Audio] Anonymization Techniques k Anonymity l Diversity Ensures each record in a dataset is indistinguishable from at least k other records. Guarantees a minimum number of distinct values for sensitive attributes within each anonymized group. t Closeness Requires the distribution of sensitive attributes within each group to be similar to the overall distribution..

[Audio] Differential Privacy Add Noise Random noise is added to the data to mask individual records. Privacy Budget A parameter that limits the amount of privacy loss allowed for each query. Privacy Guarantee Guarantees that the results of data analysis are unlikely to reveal information about individuals..

[Audio] Secure Multiparty Computation Parties Data Computation Alice Private Data A Jointly compute on data without revealing private inputs. Bob Private Data B Securely generate the result while preserving privacy..

[Audio] Homomorphic Encryption Data Encryption Data is encrypted before being processed. Compute on Ciphertext Operations can be performed directly on encrypted data. Decrypt Result Only the result of the computation is decrypted..

[Audio] Conclusion and Future Directions Privacy preserving data mining is a rapidly evolving field. As data becomes increasingly complex, new techniques will continue to be developed to ensure both privacy and insights..