Research On Locally Differentially Private Mechanisms For Data Publishing In Crowdsensing Systems

Sensitive information contained in the crowdsourcing data leads to potential privacy leakage under the data publishing procedure in crowdsensing systems.The goal of this dissertation is to design and implement privacy-preserving mechanisms for data publishing in the crowdsensing systems with local differential privacy.This dissertation investigates privacy-preserving data publishing mechanisms from the aspects of the collection of data,the diversity of data types,the personalized privacy concerns and data analysis demands.As existing privacy-preserving mechanisms cannot effectively deal with missing data,correlations in key-value data,personalized privacy-preserving concerns and the limitations in data analysis,this dissertation proposes several efficient privacy-preserving mechanisms to address these issues.Specifically,the main contributions of this dissertation are as follows.· The Bi Sample,a bi-directional perturbation mechanism for data perturbation,is proposed to avoid privacy leakage and statistical biases caused by the missing data.By introducing the positive sampling and negative sampling techniques,the Bi Sample mechanism can be used for missing data perturbation under local differential privacy.Theoretical analysis shows that Bi Sample can avoid statistical bias.· The perturbation mechanism for correlation analysis is proposed for key-value data,where keys are categorical and values are numerical.For the first time,this dissertation defines the frequency correlation and mean correlation analysis for key-value data.The proposed Indexing One-Hot mechanism can be used for correlation publishing under the protection of local differential privacy.· A datautility optimization framework is proposed under personalized local differ-ential privacy.By using a discretization–expansion–perturbation scheme,the proposed Stepwise mechanism can achieve unbiased statistical data publishing with local differential privacy guarantees.Theoretically,it shows that a highly-private Stepwise mechanism can be achieved when using Generalized Randomized Response under a lowly-private Stepwise mechanism.This property is then used to design data recycling mechanism for datautility optimization with personalized local differential privacy.· The distance-aware encoding mechanisms and corresponding clustering algorithm are proposed to address the limitations of current data analyzing techniques.By using Randomized Response in the anonymized space,the proposed distance-aware encoding mechanism can achieve strict privacy-preserving guarantees.Based on the distance-aware property,a non-interactive clustering algorithm is introduced for datamining in the crowdsensing system.In summary,this dissertation proposes a series of perturbation mechanisms with local differential privacy for data publishing in crowdsensing systems.The techniques and mechanisms proposed in this dissertation can provide theoretical support when applying local differential privacy in the crowdsensing system.