3 papers
cs.CR2020
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…
cs.DB2018
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…
cs.CV2018
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…