Balancing Privacy-Utility of Differential Privacy Mechanism: A Collaborative Perspective

Abstract

Differential privacy mechanism can maintain privacy-utility monotonicity. Thus, differential privacy mechanism does not obtain privacy-utility balance for numerical data. To this end, we provide privacy-utility balance of differential privacy mechanism with the collaborative perspective in this paper. First, we constructed the collaborative model achieving privacy-utility balance of differential privacy mechanism. Second, we presented the collaborative algorithm of differential privacy mechanism under our collaborative model. Third, our theoretical analysis showed that the collaborative algorithm of differential privacy mechanism could keep privacy-utility balance. Finally, our experimental results demonstrated that the collaborative differential privacy mechanism can maintain privacy-utility balance. Thus, we provide a new collaborative model to solve the privacy-utility balance problem of differential privacy mechanism. Our collaborative algorithm is easy to apply to query processing of numerical data.

Readmission rate prediction using differential privacy technology and multilayer perceptron

“…Their study emphasizes the role of privacy-preserving monotonicity in evaluating these trade-offs and explores methods for achieving optimal balances under various models. Liu et al proposed a collaborative model and algorithm for achieving a privacy-utility balance in differential privacy mechanisms, specifically for numerical data query processing [17]. Utilizing the T-Drive taxi trajectory dataset, their experiments showed that the collaborative algorithm can maintain a privacy-utility balance and achieve the required privacy-preserving and approximate data utility.…”

Data Privacy and Security in Big Data: A Comparative Analysis

“…Differential privacy offers a strong privacy guarantee, even in the presence of powerful adversaries. However, achieving a balance between privacy and data utility remains a challenge, as excessive noise can affect data accuracy and analytical results [15]. Homomorphic Encryption: Homomorphic encryption enables computation on encrypted data without the need for decryption, preserving data privacy.…”

“…To promote responsible data-driven practices while safeguarding privacy rights, organizations can tailor their privacy strategies to suit specific data contexts [15]. For scenarios requiring strict privacy preservation, approaches like differential privacy or homomorphic encryption may be more suitable, while less sensitive data sets could benefit from anonymization techniques with reduced computational overhead.…”

“…For scenarios requiring strict privacy preservation, approaches like differential privacy or homomorphic encryption may be more suitable, while less sensitive data sets could benefit from anonymization techniques with reduced computational overhead. Employing a combination of privacy approaches within a comprehensive privacy framework can also be advantageous in addressing diverse privacy needs across different data types and use cases [15].…”