PrivKVM*: Revisiting Key-Value Statistics Estimation With Local Differential Privacy

Ye, Qingqing; Hu, Haibo; Meng, Xiaofeng; Zheng, Huadi; Huang, Kerson; Fang, Chengfang; Shi, Jie

doi:10.1109/tdsc.2021.3107512

Cited by 28 publications

(13 citation statements)

References 45 publications

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“…To the best of our knowledge, PrivKVM * [9] is the state-of-the-art LDP framework for key-value data collection that can support frequency estimation of key and distribution estimation of value. We first briefly describe the mechanism and then summarize the main differences between our work and PrivKVM * .…”

Section: Privkvm *mentioning

confidence: 99%

“…Sampling protocols are widely used in existing DP mechanisms for key-value data perturbation [7][8][9]. However, they either do not support distribution estimation or do not work well on large domain sizes.…”

Section: Our Sampling Protocolmentioning

confidence: 99%

“…Early LDP work mainly focuses on simple statistical queries such as frequency estimation on categorical data [4] and mean estimation [5,6] on numerical data. Nowadays, LDP is also applied for hybrid data types or queries [7][8][9], for example, key-value data, which has categorical data and numerical data simultaneously and is widely used in practice. e following examples show the potential applications of key-value data:…”

Section: Introductionmentioning

confidence: 99%

“…ey design mechanisms to support two estimation tasks: (1) the frequency of the key and (2) the mean estimation of the value. Recently, Ye et al [9] expand the previous work and are the first to propose PrivKVM * to estimate the frequency of the key and the distribution of the value. It discretizes the domain into many bins and reports which bin contains the private value using categorical frequency oracle (e.g., GRR [10]) while considering the correlation between keys and values.…”

Section: Introductionmentioning

confidence: 99%

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Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Yan

Zheng

et al. 2022

Security and Communication Networks

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Local differential privacy (LDP) is a promising privacy-preserving technology from users’ perspective, as users perturb their private information locally before reporting to the aggregator. We study the problem of collecting heterogeneous data, that is, key-value pairs under LDP, which is widely involved in real-world applications. Although previous LDP work on key-value data collection achieves a good utility on frequency estimation of key and distribution estimation of value, they have three downfalls: (1) existing work perturbs numerical value in a discrete manner that does not exploit the ordinal nature of the numerical domain and lead to poor accuracy, (2) they do not lead to improved privacy budget composition and consume more privacy budget than necessary to achieve the given privacy level, and (3) the frequency estimation of the key is not the most accurate due to the lack of consistency requirement. In this paper, we propose a novel mechanism to collect key-value data under LDP leveraging the numerical nature of the domain and result in better utility. Due to our correlated perturbation, the mechanism consumes less privacy budget than previous work while keeping the privacy level. We also adopt consistency as the postprocessing, which is applied to the estimated key frequency to further improve the accuracy. Comprehensive experiments demonstrate that our approach consistently outperforms the state-of-the-art mechanisms under the same LDP guarantee.

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Section: Privkvm *mentioning

confidence: 99%

Section: Our Sampling Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Yan

Zheng

et al. 2022

Security and Communication Networks

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“…Chen et al [15] achieved optimal error and succinct communication for the 1-sparse case. The PrivKVM work [49] proposed an interactive protocol for vector mean estimation but it suffers from at least √ d error; the approach was later improved [50] but the protocol is still interactive.…”

Section: Additional Related Workmentioning

confidence: 99%

Locally Differentially Private Sparse Vector Aggregation

Zhou¹,

Wang²,

Chan³

et al. 2021

Preprint

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Vector mean estimation is a central primitive in federated analytics. In vector mean estimation, each user i ∈ [n] holds a real-valued vector v i ∈ [−1, 1] d , and a server wants to estimate the mean of all n vectors. Not only so, we would like to protect each individual user's privacy. In this paper, we consider the k-sparse version of the vector mean estimation problem, that is, suppose that each user's vector has at most k non-zero coordinates in its d-dimensional vector, and moreover, k d. In practice, since the universe size d can be very large (e.g., the space of all possible URLs), we would like the per-user communication to be succinct, i.e., independent of or (poly-)logarithmic in the universe size.In this paper, we are the first to show matching upper-and lower-bounds for the k-sparse vector mean estimation problem under local differential privacy. Specifically, we construct new mechanisms that achieve asymptotically optimal error as well as succinct communication, either under user-level-LDP or event-level-LDP. We implement our algorithms and evaluate them on synthetic as well as real-world datasets. Our experiments show that we can often achieve one or two orders of magnitude reduction in error in comparison with prior works under typical choices of parameters, while incurring insignificant communication cost.

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Local Differential Privacy for correlated location data release in ITS

Chong,

Malip

2024

Computer Networks

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PrivKVM*: Revisiting Key-Value Statistics Estimation With Local Differential Privacy

Cited by 28 publications

References 45 publications

Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Locally Differentially Private Sparse Vector Aggregation

Local Differential Privacy for correlated location data release in ITS

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