A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

Achieving Differential Privacy of Genomic Data Releasing via Belief Propagation

Privacy preserving data releasing is an important problem for reconciling data openness with individual privacy. The state-of-the-art approach for privacy preserving data release is differential privacy, which offers powerful privacy guarantee without confining assumptions about the background knowledge about attackers. For genomic data with huge-dimensional attributes, however, current approaches based on differential privacy are not effective to handle. Specifically, amount of noise is required to be injected to genomic data with tens of million of SNPs （Single Nucleotide Polymorphisms）, which would significantly degrade the utility of released data. To address this problem, this paper proposes a differential privacy guaranteed genomic data releasing method. Through executing belief propagation on factor graph, our method can factorize the distribution of sensitive genomic data into a set of local distributions. After injecting differential-privacy noise to these local distributions, synthetic sensitive data can be obtained by sampling on noise distribution. Synthetic sensitive data and factor graph can be further used to construct approximate distribution of non-sensitive data. Finally, non-sensitive genomic data is sampled from the approximate distribution to construct a synthetic genomic dataset.

Inference Attacks on Genomic Data Based on Probabilistic Graphical Models

The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information,leading to one of the most important concerns—genomic data privacy.Since personal biomedical data are highly correlated with relatives,with the increasing availability of genomes and personal traits online(i.e.,leakage unwittingly,or after their releasing intentionally to genetic service platforms),kin-genomic data privacy is threatened.We propose new inference attacks to predict unknown Single Nucleotide Polymorphisms(SNPs)and human traits of individuals in a familial genomic dataset based on probabilistic graphical models and belief propagation.With this method,the adversary can predict the unobserved genomes or traits of targeted individuals in a family genomic dataset where some individuals’genomes and traits are observed,relying on SNP-trait association from Genome-Wide Association Study(GWAS),Mendel’s Laws,and statistical relations between SNPs.Existing genome inferences have relatively high computational complexity with the input of tens of millions of SNPs and human traits.Then,we propose an approach to publish genomic data with differential privacy guarantee.After finding an approximate distribution of the input genomic dataset relying on Bayesian networks,a noisy distribution is obtained after injecting noise into the approximate distribution.Finally,synthetic genomic dataset is sampled and it is proved that any query on synthetic dataset satisfies differential privacy guarantee.

Optimization-Based Fragmental Transmission Method for Video Data in Opportunistic Networks

Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.