Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy

By Zhan Qin, Y. Yang, Ting Yu, Issa M. Khalil, Xiaokui Xiao, Kui Ren. 2016

344 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Privacy & Security, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Privacy & Security

In Algorithms & Complexity

Citations in the corpus

Breaking the Communication-Privacy-Accuracy Trilemma (2020, background): “Frequency estimation is also closely related to heavy hitter estimation [10], [13], [17], [36], [37], [38], [39], where the goal is to discover symbols that appear frequently in a given data set and estimate their frequencies.”
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity (2018, background): “Due to their attractive trust model, LDP mechanisms have recently received signiﬁcant industrial adoption for the privacy-preserving collection of heavy hitters [EPK14, App17, DKY17], as well as increased academic attention [BS15, BNST17, QYY + 16, WBLJ17].”
Marginal Release Under Local Differential Privacy (2017, background): “Qin et al. adapt this approach to the related problem of identifying heavy hitters within set valued data [31].”
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, methodology): “[34] for heavy hitter estimation with local differential privacy, in which they provide a utility”
Collecting and Analyzing Multidimensional Data with Local Differential Privacy (2019, background): “Various data analytics and machine learning problems have been studied under LDP, such as probability distribution estimation [15], [23], [30], [32], [42], heavy hitter discovery [4], [7], [33], [39], frequent new term discovery [37], frequency estimation [5], [38], frequent itemset mining [40], mar”
Heavy Hitters and the Structure of Local Privacy (2017, background): “The heavy hitters problem is perhaps the most well-studied problem in local di↵erential privacy [4, 17, 24, 3, 27].”
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters (2019, background): “The problem of heavy hitter estimation under ε -LDP has also received a lot of attention [35, 15, 36, 25, 37, 16, 38].”
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning (2018, background): “Local differential privacy (LDP): LDP [33–40] is a technique based on ε-differential privacy [41] to protect privacy of an individual user’s data record, i.”
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, methodology): “ivious transfer, fully or partially homomorphic encryption, and functional encryption. Prior work has shown that it is possible to maintain the same DP guarantee achieved by local differential privacy[24, 33], i.e., each party adds its own noise independently, and uses SMC to hide individual inputs.”
PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy (2018, methodology): “Besides, there exist some works [9], [15], [16], [36] focusing on publishing heavy hitters.”
CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy (2018, background): “Previous works on LDP focus on estimating the frequencies of frequent values the user possesses [6, 7, 16, 30, 37–39].”
Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs (2020, background): “For example, the histogram distribution [27], the general graph structures [28], and the frequent items [29], [30].”
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge (2018, methodology): “Local differential privacy (LDP) [27], [5], [20], [22], [30], [3], [12], [13], [34], [29], [23], [8], [24], [14], [6], [28], [32], [35], [1], a privacy protection mechanism based on differential privacy [15], can address both challenges.”
Answering Multi-Dimensional Analytical Queries under Local Differential Privacy (2019, background): “, finding heavy hitters [4, 6, 36], frequent itemset mining [29, 37], and histogram estimation [24, 34, 38].”
Locally Differentially Private Heavy Hitter Identification (2017, background): “For example, mean estimation [11], [27], frequent itemset mining [29], [37], releasing marginals under LDP [10], [30], [41], key-value pair estimation [40], evolving data monitoring [15], [22], and empirical risk minimization [31], [32].”
Locally Differentially Private Data Collection and Analysis (2019, background): “Current researches focus on many related problems under the LDP model, such as mean/frequency estimation [15, 39, 48], probability distribution estimation [22, 56, 47], heavy hitter identification [5, 41, 8], itemset mining [50], marginal distribution release [12, 60], and empirical risk minimizatio”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “Local differential privacy (LDP) [23], [30], [73], as an adaptation of DP, is defined under the setting where the user does not trust anyone (not even the central data collector).”
Toward Distribution Estimation under Local Differential Privacy with Small Samples (2018, background): “, histogram, heavy hitters [39]) of the original data or provides a service (e.”