A Privacy-Preserving Data Mining Approach in Multi-Dimensional Data Set based on the Random and Cumulative Integrated Noise
Keywords:
Cumulative noise, data mining, data perturbation, privacy preservation, RPCNAbstract
Since the emergence of Big Data, data mining has changed for the better. We now have unprecedented opportunities to discover new knowledge and support decision-making. While Big Data methods are gaining traction, privacy is becoming a major concern. In order to overcome these difficulties, we employ new data perturbation approaches based on random translation and random projections, as well as additive noise. In addition, we assess the performance of different data perturbation modes and their corresponding attack modes using input-output maximum a posteriori (MAP) attacks. Firstly, we evaluate random projections for online classifications. Two data perturbation modalities are assessed: random translation (RT) and random projection (R). Independent noise (RPIN), or cumulative noise (RCPN) are also assessed. Our results show that a combination of 2 MAP attacks (MAX (A-RT, A-RCPN-1)) vs. RPCN method is the most efficient. As the data record moves away from the known data record, the attack becomes less efficient, indicating that over time, RPCN gradually improves data privacy. Thanks to our work, we can apply these perturbation techniques to more than just classification tasks. We can apply them to cluster, anomaly detection and regression, which opens up new research directions. We are also exploring privacy preservation techniques that are tailored to the streaming nature of real-world data sets to improve privacy for nominal data.
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