TY - CHAP
T1 - Utility-preserving privacy mechanisms for counting queries
AU - Fernandes, Natasha
AU - Lefki, Kacem
AU - Palamidessi, Catuscia
PY - 2019
Y1 - 2019
N2 - Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the noise is added to the result of queries on the dataset, whereas in LPD the noise is added directly on the individual records, before being collected. The main advantage of LPD with respect to DP is that it does not need to assume a trusted third party. The main disadvantage is that the trade-off between privacy and utility is usually worse than in DP, and typically to retrieve reasonably good statistics from the locally sanitized data it is necessary to have a huge collection of them. In this paper, we focus on the problem of estimating counting queries from collections of noisy answers, and we propose a variant of LDP based on the addition of geometric noise. Our main result is that the geometric noise has a better statistical utility than other LPD mechanisms from the literature.
AB - Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the noise is added to the result of queries on the dataset, whereas in LPD the noise is added directly on the individual records, before being collected. The main advantage of LPD with respect to DP is that it does not need to assume a trusted third party. The main disadvantage is that the trade-off between privacy and utility is usually worse than in DP, and typically to retrieve reasonably good statistics from the locally sanitized data it is necessary to have a huge collection of them. In this paper, we focus on the problem of estimating counting queries from collections of noisy answers, and we propose a variant of LDP based on the addition of geometric noise. Our main result is that the geometric noise has a better statistical utility than other LPD mechanisms from the literature.
UR - http://www.scopus.com/inward/record.url?scp=85068833532&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21485-2_27
DO - 10.1007/978-3-030-21485-2_27
M3 - Chapter
AN - SCOPUS:85068833532
SN - 9783030214845
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 495
BT - Models, languages, and tools for concurrent and distributed programming
A2 - Boreale, Michele
A2 - Corradini, Flavio
A2 - Loreti, Michele
A2 - Pugliese, Rosario
PB - Springer-VDI-Verlag GmbH & Co. KG
CY - Cham, Switzerland
ER -