Victor Perrier (Data61, CSIRO and ISAE-SUPAERO), Hassan Jameel Asghar (Macquarie University and Data61, CSIRO), Dali Kaafar (Macquarie University and Data61, CSIRO)

We present a differentially private mechanism to display statistics (e.g., the moving average) of a stream of real valued observations where the bound on each observation is either too conservative or unknown in advance. This is particularly relevant to scenarios of real-time data monitoring and reporting, e.g., energy data through smart meters. Our focus is on real-world data streams whose distribution is light-tailed, meaning that the tail approaches zero at least as fast as the exponential distribution. For such data streams, individual observations are expected to be concentrated below an unknown threshold. Estimating this threshold from the data can potentially violate privacy as it would reveal particular events tied to individuals. On the other hand an overly conservative threshold may impact accuracy by adding more noise than necessary. We construct a utility optimizing differentially private mechanism to release this threshold based on the input stream. Our main advantage over the state-of-the-art algorithms is that the resulting noise added to each observation of the stream is scaled to the threshold instead of a possibly much larger bound; resulting in considerable gain in utility when the difference is significant. Using two real-world datasets, we demonstrate that our mechanism, on average, improves the utility by a factor of 3.5 on the first dataset, and 9 on the other. While our main focus is on continual release of statistics, our mechanism for releasing the threshold can be used in various other applications where a (privacy-preserving) measure of the scale of the input distribution is required.

View More Papers

Countering Malicious Processes with Process-DNS Association

Suphannee Sivakorn (Columbia University), Kangkook Jee (NEC Labs America), Yixin Sun (Princeton University), Lauri Korts-Pärn (Cyber Defense Institute), Zhichun Li (NEC Labs America), Cristian Lumezanu (NEC Labs America), Zhenyu Wu (NEC Labs America), Lu-An Tang (NEC Labs America), Ding Li (NEC Labs America)

Read More

Balancing Image Privacy and Usability with Thumbnail-Preserving Encryption

Kimia Tajik (Oregon State University), Akshith Gunasekaran (Oregon State University), Rhea Dutta (Cornell University), Brandon Ellis (Oregon State University), Rakesh B. Bobba (Oregon State University), Mike Rosulek (Oregon State University), Charles V. Wright (Portland State University), Wu-Chi Feng (Portland State University)

Read More

NAUTILUS: Fishing for Deep Bugs with Grammars

Cornelius Aschermann (Ruhr-Universität Bochum), Tommaso Frassetto (Technische Universität Darmstadt), Thorsten Holz (Ruhr-Universität Bochum), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Daniel Teuchert (Ruhr-Universität Bochum)

Read More

CRCount: Pointer Invalidation with Reference Counting to Mitigate Use-after-free...

Jangseop Shin (Seoul National University and Inter-University Semiconductor Research Center), Donghyun Kwon (Seoul National University and Inter-University Semiconductor Research Center), Jiwon Seo (Seoul National University and Inter-University Semiconductor Research Center), Yeongpil Cho (Soongsil University), Yunheung Paek (Seoul National University and Inter-University Semiconductor Research Center)

Read More