The Trade-off Between Privacy and Utility in Local Differential Privacy

Li, Mengqian; Tian, Youliang; Zhang, Junpeng; Ding, Fan; Zhao, Dongmei

doi:10.1109/nana53684.2021.00071

Cited by 1 publication

(1 citation statement)

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“…This problem is more relevant in the case of heavy hitters (Zhu et al 2020), where the data for certain individuals is present more frequently in the distribution. Another consideration when DP is employed is the balance of privacy achieved by noise addition and the utility of the perturbed data (Li et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Sarve: synthetic data and local differential privacy for private frequency estimation

Varma

Chauhan

Singh³

2022

Cybersecurity

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The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.

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Section: Introductionmentioning

confidence: 99%