Privacy Leakage over Dependent Attributes in One-Sided Differential Privacy
This addresses privacy risks in applications like building management systems where data dependencies exist, but it is an incremental extension of the OSDP framework.
The paper tackles the problem of privacy leakage in One-Sided Differential Privacy (OSDP) when records are dependent, quantifying the leakage and showing how to optimize the trade-off between utility and privacy.
Providing a provable privacy guarantees while maintaining the utility of data is a challenging task in many real-world applications. Recently, a new framework called One-Sided Differential Privacy (OSDP) was introduced that extends existing differential privacy approaches. OSDP increases the utility of the data by taking advantage of the fact that not all records are sensitive. However, the previous work assumed that all records are statistically independent from each other. Motivated by occupancy data in building management systems, this paper extends the existing one-sided differential privacy framework. In this paper, we quantify the overall privacy leakage when the adversary is given dependency information between the records. In addition, we show how an optimization problem can be constructed that efficiently trades off between the utility and privacy.