Locating a Small Cluster Privately

Nissim, Kobbi; Stemmer, Uri; Vadhan, Salil

doi:10.1145/2902251.2902296

Cited by 31 publications

(63 citation statements)

References 23 publications

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“…The problem of clustering was studied under centralized DP [ 127 , 128 , 129 ]. With LDP model, Nissim and Stemmer [ 130 ] conducted 1-clustering by finding a minimum enclosing ball.…”

Section: Machine Learning With Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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“…The problem of clustering was studied under centralized DP [ 127 , 128 , 129 ]. With LDP model, Nissim and Stemmer [ 130 ] conducted 1-clustering by finding a minimum enclosing ball.…”

Section: Machine Learning With Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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“…Differential privacy [9] is a robust, precise and mathematically rigorous notion of privacy, and it has emerged in a recent line of work that seeks private data analysis (See [4,5,22,23,20,6,3,21,10,12,8]). Differential privacy promises that very little about any specific individual can be learned in the data analysis process irrespective of the attacker's prior knowledge, information source and other datasets.…”

Section: Differential Privacymentioning

confidence: 99%

“…Our algorithm is based on a subroutine that given a database P ∈ X d and an integer t, approximately returns the center of the smallest ball that contains at least t points from P . We use a private approximation algorithm to this problem, that was suggested recently by K. Nissim et al [23] as the following.…”

Section: Background Definitions and Theoremsmentioning

confidence: 99%

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Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks

Feldman

Xiang

Zhu

et al. 2017

Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

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Mobile sensor networks are a great source of data. By collecting data with mobile sensor nodes from individuals in a user community, e.g. using their smartphones, we can learn global information such as traffic congestion patterns in the city, location of key community facilities, and locations of gathering places. Can we publish and run queries on mobile sensor network databases without disclosing information about individual nodes?Differential privacy is a strong notion of privacy which guarantees that very little will be learned about individual records in the database, no matter what the attackers already know or wish to learn. Still, there is no practical system applying differential privacy algorithms for clustering points on real databases. This paper describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, we give the first k-means clustering algorithm that is both differentially private, and has an approximation error that depends sub-linearly on the data's dimension d. Previous results introduced errors that are exponential in d.We implemented this algorithm and used it to create differentially private location data from GPS tracks. Specifically our algorithm allows clustering GPS databases generated from mobile nodes, while letting the user control the introduced noise due to privacy. We provide experimental results for the system and algorithms, and compare them to existing techniques. To the best of our knowledge, this is the first practical system that enables differentially private clustering on real data.

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“…Ahmed et al [10] propose a privacy preserving mechanism for publishing and clustering social network graph data, which satisfies differential privacy guarantees by utilizing a combination of theory of random matrix and that of differential privacy. Meanwhile, Day et al [11] investigated the problem of publishing the degree distribution of a graph under nodeDP by exploring the projection approach to reduce the sensitivity. They proposed two approaches based on aggregation and cumulative histogram to publish the degree distribution.…”

Section: Related Workmentioning

confidence: 99%

Counterterrorism: Privately Clustering a Radical Social Network Data

Boujmil¹,

Tagmouti²,

Raissouni³

2017

TMLAI

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The tradeoff between the needed or essential gathering and analysis of personal data and the privacy rights of individuals is now an important requirement under any counterterrorism program. The most famous and controversial recent example is the revelation that US intelligence agencies systemically engage in "bulk collection" of civilian "metadata" detailing telephonic and other types of communication and activities, with the alleged purpose of monitoring and thwarting terrorist activity. Differential privacy provides one of the strongest privacy guarantees up to now. In this paper, we present a new provably privacypreserving algorithm able to identify and take action upon members of the targeted subpopulation. Meanwhile, avoiding compromising the privacy of the patriot subpopulation. It is a new algorithm for search methods which use a new combination of nodes social similarity and differential privacy.

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Locating a Small Cluster Privately

Cited by 31 publications

References 23 publications

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Coresets for differentially private k-means clustering and applications to privacy in mobile sensor networks

Counterterrorism: Privately Clustering a Radical Social Network Data

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