17 citations · 108 across the 15 of their papers we have counts for
15 papers
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
Andrea Gadotti, Florimond Houssiau, Meenatchi Sundaram Muthu Selva Annamalai +1
Behavioral data generated by users' devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained…
A Crypto-Assisted Approach for Publishing Graph Statistics with Node Local Differential Privacy
Shang Liu, Yang Cao, Takao Murakami +1
Publishing graph statistics under node differential privacy has attracted much attention since it provides a stronger privacy guarantee than edge differential privacy. Existing wor…
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Subgraph counting is fundamental for analyzing connection patterns or clustering tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to subgraph…
Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
Seira Hidano, Takao Murakami
Differentially private GNNs (Graph Neural Networks) have been recently studied to provide high accuracy in various tasks on graph data while strongly protecting user privacy. In pa…
Communication-Efficient Triangle Counting under Local Differential Privacy
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Triangle counting in networks under LDP (Local Differential Privacy) is a fundamental task for analyzing connection patterns or calculating a clustering coefficient while strongly…
Designing a Location Trace Anonymization Contest
Takao Murakami, Hiromi Arai, Koki Hamada +13
For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest that deals with a long trace (400 events p…
TransMIA: Membership Inference Attacks Using Transfer Shadow Training
Seira Hidano, Takao Murakami, Yusuke Kawamoto
Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different t…
Locality Sensitive Hashing with Extended Differential Privacy
Natasha Fernandes, Yusuke Kawamoto, Takao Murakami
Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while k…
Locally Differentially Private Analysis of Graph Statistics
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in…
Toward Evaluating Re-identification Risks in the Local Privacy Model
Takao Murakami, Kenta Takahashi
LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local mo…
Local Distribution Obfuscation via Probability Coupling
Yusuke Kawamoto, Takao Murakami
We introduce a general model for the local obfuscation of probability distributions by probabilistic perturbation, e.g., by adding differentially private noise, and investigate its…
Local Obfuscation Mechanisms for Hiding Probability Distributions
Yusuke Kawamoto, Takao Murakami
We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probabili…
On the Anonymization of Differentially Private Location Obfuscation
Yusuke Kawamoto, Takao Murakami
Obfuscation techniques in location-based services (LBSs) have been shown useful to hide the concrete locations of service users, whereas they do not necessarily provide the anonymi…
Utility-Optimized Local Differential Privacy Mechanisms for Distribution Estimation
Takao Murakami, Yusuke Kawamoto
LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal data (e.g., distribution underlying the data) while protecting users' privacy. Although…
Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification
Takao Murakami, Tetsushi Ohki, Yosuke Kaga +2
A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature re…