PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Distributed stream monitoring has numerous potential applications in future smart cities. Communication efficiency, and data privacy are two main challenges for distributed stream monitoring services. In this paper, we propose PriStream, the first communication-efficient and privacy-preserving distributed stream monitoring system for thresholded PERCENTILE aggregates. PriStream allows the monitoring service provider to evaluate an arbitrary function over a desired percentile of distributed data reports and monitor when the output exceeds a predetermined system threshold. Detailed theoretical analysis and evaluations show that PriStream has high accuracy and communication efficiency, and differential privacy guarantees under a strong adversary model.

[1]  Xu Chen,et al.  SYNERGY: A game-theoretical approach for cooperative key generation in wireless networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[2]  Elaine Shi,et al.  Privacy-Preserving Aggregation of Time-Series Data , 2011, NDSS.

[3]  Chi Zhang,et al.  Secure Spatial Top-k Query Processing via Untrusted Location-Based Service Providers , 2015, IEEE Transactions on Dependable and Secure Computing.

[4]  Rajeev Rastogi,et al.  Efficient Detection of Distributed Constraint Violations , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[5]  Héctor Pomares,et al.  mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications , 2014, IWAAL.

[6]  Yanchao Zhang,et al.  Privacy-Preserving Crowdsourced Spectrum Sensing , 2018, IEEE/ACM Transactions on Networking.

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

[8]  David K. Y. Yau,et al.  Proactive fault-tolerant aggregation protocol for privacy-assured smart metering , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[9]  Daniel A. Spielman,et al.  Spectral Graph Theory and its Applications , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[10]  Graham Cormode,et al.  Approximate continuous querying over distributed streams , 2008, TODS.

[11]  Claude Castelluccia,et al.  I Have a DREAM! (DiffeRentially privatE smArt Metering) , 2011, Information Hiding.

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

[13]  Chrisil Arackaparambil,et al.  Functional Monitoring without Monotonicity , 2009, ICALP.

[14]  Assaf Schuster,et al.  Privacy-Preserving Distributed Stream Monitoring , 2014, NDSS.

[15]  Qinghua Li,et al.  Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error , 2013, Privacy Enhancing Technologies.

[16]  Assaf Schuster,et al.  A geometric approach to monitoring threshold functions over distributed data streams , 2006, Ubiquitous Knowledge Discovery.

[17]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2016, J. Priv. Confidentiality.

[18]  Shaojie Tang,et al.  Privacy-preserving data aggregation without secure channel: Multivariate polynomial evaluation , 2013, 2013 Proceedings IEEE INFOCOM.

[19]  Assaf Schuster,et al.  Monitoring Distributed Streams using Convex Decompositions , 2015, Proc. VLDB Endow..

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

[21]  Rongxing Lu,et al.  A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications , 2015, IEEE Internet of Things Journal.

[22]  Rui Zhang,et al.  PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems , 2010, 2010 Proceedings IEEE INFOCOM.

[23]  Qun Li,et al.  Fog Computing: Platform and Applications , 2015, 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[24]  Qinghua Li,et al.  Efficient and privacy-preserving data aggregation in mobile sensing , 2012, 2012 20th IEEE International Conference on Network Protocols (ICNP).

[25]  Paul Francis,et al.  Towards Statistical Queries over Distributed Private User Data , 2012, NSDI.

[26]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[27]  Marc Joye,et al.  A Scalable Scheme for Privacy-Preserving Aggregation of Time-Series Data , 2013, Financial Cryptography.

[28]  Aniket Kate,et al.  Differentially private data aggregation with optimal utility , 2014, ACSAC '14.

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

[30]  Chi Zhang,et al.  Verifiable Privacy-Preserving Aggregation in People-Centric Urban Sensing Systems , 2013, IEEE Journal on Selected Areas in Communications.

[31]  Rui Zhang,et al.  SecureFind: Secure and Privacy-Preserving Object Finding via Mobile Crowdsourcing , 2015, IEEE Transactions on Wireless Communications.

[32]  Elaine Shi,et al.  Privacy-Preserving Stream Aggregation with Fault Tolerance , 2012, Financial Cryptography.

[33]  Daniel Keren,et al.  Sketch-based Geometric Monitoring of Distributed Stream Queries , 2013, Proc. VLDB Endow..

[34]  Chi Zhang,et al.  Secure top-k query processing via untrusted location-based service providers , 2012, 2012 Proceedings IEEE INFOCOM.

[35]  Alec Wolman,et al.  Software abstractions for trusted sensors , 2012, MobiSys '12.

[36]  Graham Cormode,et al.  Communication-efficient distributed monitoring of thresholded counts , 2006, SIGMOD Conference.

[37]  Amir Abboud,et al.  Geometric Monitoring of Heterogeneous Streams , 2014, IEEE Transactions on Knowledge and Data Engineering.

[38]  Xue Liu,et al.  PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[39]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[40]  Florian Kerschbaum,et al.  Fault-Tolerant Privacy-Preserving Statistics , 2012, Privacy Enhancing Technologies.

[41]  Rui Zhang,et al.  Privacy-preserving spatiotemporal matching , 2013, 2013 Proceedings IEEE INFOCOM.

[42]  Peng Cheng,et al.  Achieving Bilateral Utility Maximization and Location Privacy Preservation in Database-Driven Cognitive Radio Networks , 2015, 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.