A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Convex Optimization
Privacy preserving regression modelling via distributed computation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
E.cient Aggregation of encrypted data in Wireless Sensor Networks
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Pervasive Computing
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
PoolView: stream privacy for grassroots participatory sensing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
GreenGPS: a participatory sensing fuel-efficient maps application
Proceedings of the 8th international conference on Mobile systems, applications, and services
Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Review: From wireless sensor networks towards cyber physical systems
Pervasive and Mobile Computing
Sensorsafe: a framework for privacy-preserving management of personal sensory information
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
On truth discovery in social sensing: a maximum likelihood estimation approach
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Fast track article: Balancing behavioral privacy and information utility in sensory data flows
Pervasive and Mobile Computing
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Maximum likelihood analysis of conflicting observations in social sensing
ACM Transactions on Sensor Networks (TOSN)
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Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a model relating exercise and diet to amount of weight loss. While the ultimately computed model could be public, the individual input and output data traces used to construct it may be private data of participants (e.g., their individual food intake, lifestyle choices, and resulting weight). This paper proposes and experimentally studies a technique that attempts to keep such input and output data traces private, while allowing accurate model construction. This is significantly different from perturbation-based techniques in that no noise is added. The main contribution of the paper is to show a certain data transformation at the client side that helps keeping the client data private while not introducing any additional error to model construction. We particularly focus on linear regression models which are widely used in participatory sensing applications. We use the data set from a map-based participatory sensing service to evaluate our scheme. The service in question is a green navigation service that constructs regression models from participant data to predict the fuel consumption of vehicles on road segments. We evaluate our proposed mechanism by providing empirical evidence that: i) an individual data trace is generally hard to reconstruct with any reasonable accuracy, and ii) the regression model constructed using the transformed traces has a much smaller error than one based on additive data-perturbation schemes.