Conditional Analysis for Key-Value Data with Local Differential Privacy

Sun, Lin; Zhao, Jun; Ye, Xiaojun; Feng, Shuo; Wang, Teng; Bai, Tao

doi:10.48550/arxiv.1907.05014

Cited by 5 publications

(22 citation statements)

References 19 publications

(27 reference statements)

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“…Remark 3 The privacy-preserving techniques based on GRR/RR have been employed into some DP/LDP mechanisms including RAPPOR [4], SHist [31], PrivKVM [17], KVUE [32] and PCKV [18]. Technically speaking, it is necessary to set probability p to e /(e + 1) for RR to satisfy -LDP.…”

Section: Notations and Definitionsmentioning

confidence: 99%

“…As a result, there are many approaches developed to achieve differential privacy, for instance, Laplacian mechanism [8], exponential mechanism [11], randomized response [4], etc. Among which, randomized response has been widely adopted into a great number of algorithms satisfying differential privacy, such as RAPPOR [4], k-RR [20], O-RR [21], Harmony-mean [23], SHist [31], PrivKVM [17], KVUE [32] and PCKV [18], by virtue of its own merits including simplicity and understandability. As known, randomized response as a means for collecting statistical information in social science was first presented by Warner in 1965 [19], and is proved to perform well in the estimate of statistics.…”

Section: Related Workmentioning

confidence: 99%

“…As known, randomized response as a means for collecting statistical information in social science was first presented by Warner in 1965 [19], and is proved to perform well in the estimate of statistics. Since then, this technique has become the state-of-the-art approach for protecting personal privacy during collecting private data and thus has been popularly used in statistic sciences [11,17,23,31,32].…”

Section: Related Workmentioning

confidence: 99%

“…Using randomized response with binary answers as an ingredient, Ye et al studied Key-Value pair in local differential private setting [17]. It should be mentioned that the Key-Value pair is also considered in two recently published papers [32,18]. As shown previously, this kind of task is in essence closely associated with three-elements randomized response.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications

Ma¹,

Wang²

2021

Preprint

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Randomized response, as a basic building-block for differentially private mechanism, has given rise to great interest and found various potential applications in science communities. In this work, we are concerned with three-elements randomized response (RR 3 ) along with relevant applications to the analysis of weighted bipartite graph upon differentially private guarantee. We develop a principled framework for estimating statistics produced by RR 3 -based mechanisms, and then prove the corresponding estimations to be unbiased. At the same time, we study in detail several fundamental and significant members in RR 3 family, and derive the closed-form solutions to unbiased estimations. Next, we show potential applications of several RR 3 -based mechanisms into the estimation of average degree and average weighted value on weighted bipartite graph when requiring local differential privacy guarantee. In the meantime, we determine the lower bounds for choice of relevant parameters by minimizing variance of statistics in order to design optimal RR 3 -based local differential private mechanisms, with which we optimize previous protocols in the literature and put forward a version that achieves the tight bound. Last but most importantly, we observe that in the analysis of relational data such as weighted bipartite graph, a portion of privacy budget in local differential private mechanism is sometimes "consumed" by mechanism itself accidentally, resulting to a more stronger privacy guarantee than we would get by simply sequential compositions.

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Section: Notations and Definitionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications

Ma¹,

Wang²

2021

Preprint

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“…Besides, they proposed an optimized privacy budget allocation scheme to improve the utility further. Sun et al [56] propose a new idea in their preprint article. They encode the continuous numerical value as two extreme values −1 and 1, then randomly sample the value from the perturbed space, which constructed by all the possible combinations between the key and the value.…”

Section: Mean Value Estimation Over Key Value Datamentioning

confidence: 99%

Local Differential Privacy and Its Applications: A Comprehensive Survey

Yang¹,

Lyu²,

Zhao³

et al. 2020

Preprint

Self Cite

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With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of users are growing concerned about their personal information, privacy preservation has become an urgent problem to be solved and has attracted significant attention. Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years. It breaks the shackles of the trusted third party, and allows users to perturb their data locally, thus providing much stronger privacy protection. This survey provides a comprehensive and structured overview of the local differential privacy technology. We summarise and analyze state-of-the-art research in LDP and compare a range of methods in the context of answering a variety of queries and training different machine learning models. We discuss the practical deployment of local differential privacy and explore its application in various domains. Furthermore, we point out several research gaps, and discuss promising future research directions.

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KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy

Zhao,

You,

Luo

et al. 2024

CMES

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In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff. Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection, this paper presents a novel framework: the Key-Strategy Framework for Key-Value Data Collection under LDP. Initially, the Key-Strategy Unary Encoding (KS-UE) strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies; subsequently, the Key-Strategy Generalized Randomized Response (KS-GRR) strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods. Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs. Theoretically, we demonstrate that the variance of KS-UE is lower than that of existing methods. These claims are substantiated through extensive experimental evaluation on real-world datasets, confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

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Conditional Analysis for Key-Value Data with Local Differential Privacy

Cited by 5 publications

References 19 publications

Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications

Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications

Local Differential Privacy and Its Applications: A Comprehensive Survey

KSKV: Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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