Differential Private Stream Processing of Energy Consumption

A number of applications benefit from continuously releasing streams of personal data statistics. The process, however, poses significant privacy risks. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differential private data streams. OptStream is a 4-step procedure consisting of sampling, perturbation, reconstruction, and post-processing modules. The sampling module selects a small set of points to access privately in each period of interest, the perturbation module adds noise to the sampled data points to guarantee privacy, the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data's salient features. OptStream is used to release a real data stream from the largest transmission operator in Europe. Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data.

[1]  Luis Ochoa,et al.  Minimizing Energy Losses: Optimal Accommodation and Smart Operation of Renewable Distributed Generation , 2011, IEEE Transactions on Power Systems.

[2]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[3]  Layth C. Alwan,et al.  Time-Series Modeling for Statistical Process Control , 1988 .

[4]  Cynthia Dwork,et al.  Differential privacy in new settings , 2010, SODA '10.

[5]  Li Xiong,et al.  An Adaptive Approach to Real-Time Aggregate Monitoring With Differential Privacy , 2014, IEEE Trans. Knowl. Data Eng..

[6]  Aleksandar Nikolov,et al.  Pan-private algorithms via statistics on sketches , 2011, PODS.

[7]  Elaine Shi,et al.  Private and Continual Release of Statistics , 2010, ICALP.

[8]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[9]  Janardhan Kulkarni,et al.  Collecting Telemetry Data Privately , 2017, NIPS.

[10]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[11]  Úlfar Erlingsson,et al.  Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries , 2015, Proc. Priv. Enhancing Technol..

[12]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[13]  George J. Pappas,et al.  Optimality of the Laplace Mechanism in Differential Privacy , 2015, ArXiv.

[14]  Pascal Van Hentenryck,et al.  Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately - Releasing Optimal Power Flow Benchmarks Privately , 2018, CPAIOR.

[15]  Vaidy S. Sunderam,et al.  FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling , 2013, SIGMOD '13.

[16]  Suman Nath,et al.  Differentially private aggregation of distributed time-series with transformation and encryption , 2010, SIGMOD Conference.

[17]  Pascal Van Hentenryck,et al.  Constrained-Based Differential Privacy for Mobility Services , 2018, AAMAS.

[18]  Ashwin Machanavajjhala,et al.  PeGaSus: Data-Adaptive Differentially Private Stream Processing , 2017, CCS.

[19]  Catuscia Palamidessi,et al.  Geo-indistinguishability: differential privacy for location-based systems , 2012, CCS.

[20]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[21]  Kunal Talwar,et al.  Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[22]  Catuscia Palamidessi,et al.  Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.

[23]  Aleksandar Nikolov,et al.  Private decayed predicate sums on streams , 2013, ICDT '13.

[24]  Yan Zhang,et al.  RescueDP: Real-time spatio-temporal crowd-sourced data publishing with differential privacy , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[25]  Moni Naor,et al.  Differential privacy under continual observation , 2010, STOC '10.

[26]  Stavros Papadopoulos,et al.  Differentially Private Event Sequences over Infinite Streams , 2014, Proc. VLDB Endow..