Fair and Differentially Private Distributed Frequency Estimation

Yang, Mengmeng; Tjuawinata, Ivan; Lam, Kwok-Yan; Zhu, Tianqing; Zhao, Jun

doi:10.48550/arxiv.2104.05974

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…In a previous section, we have discussed the advantages offered by sampling to improve the offered privacy provisions. Privacy-preserved frequency estimation has also been achieved with a combination of sampling and Multi-Party Computation (MPC), a cryptographic protocol (Yang et al 2021).…”

Section: Literature Surveymentioning

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: Literature Surveymentioning

confidence: 99%

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

Varma

Chauhan

Singh³

2022

Cybersecurity

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“…Relaxed LDP was studied in [37] that proposes E-LDP where E defines heterogeneous privacy guarantees for different pairs of private data values for significant utility gains in answering linear and multi-dimensional range queries. A recent work used sampling-based frequency estimation with fairness constraints which provides some level of privacy protection with good utility when the number of clients is small [38]. A main challenge with these methods is that ensuring true randomness is a difficult task, so the success of random sampling is dependent on the data.…”

Section: Related Workmentioning

confidence: 99%

Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures

Vatsalan

Bhaskar²,

Kâafar

2022

IEEE Trans. Knowl. Data Eng.

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Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR [1] and Apple's count-mean sketch (CMS) algorithm [2] propose privacy preserving mechanisms for count estimation in large volumes of data using probabilistic data structures like counting Bloom filter and CMS. However, these existing methods fall short in providing a sound solution for real-time streaming data applications. Since the size of the data structure in these methods is not adaptive to the volume of the streaming data, the utility (accuracy of the count estimate) can suffer over time due to increased false positive rates. Further, the lookup operation needs to be highly efficient to answer count estimate queries in real-time. More importantly, the local Differential privacy mechanisms used in these approaches to provide privacy guarantees come at a large cost to utility (impacting the accuracy of count estimation). In this work, we propose a novel (local) Differentially private mechanism that provides high utility for the streaming data count estimation problem with similar or even lower privacy budgets while providing: a) fuzzy counting to report counts of related or similar items (for instance to account for typing errors and data variations), and b) improved querying efficiency to reduce the response time for real-time querying of counts. Our algorithm uses a combination of two probabilistic data structures Cuckoo filter and Bloom filter. We provide formal proofs for privacy and utility guarantees and present extensive experimental evaluation of our algorithm using real and synthetic English words datasets for both the exact and fuzzy counting scenarios. Our privacy preserving mechanism substantially outperforms the prior work in terms of lower querying time, significantly higher utility (accuracy of count estimation) under similar or lower privacy guarantees, at the cost of communication overhead.

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Fair and Differentially Private Distributed Frequency Estimation

Cited by 2 publications

References 29 publications

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

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

Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures

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