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Preventing Interval-Based Inference by Random Data Perturbation [chapter]

Yingjiu Li, Lingyu Wang, Sushil Jajodia
2003 Lecture Notes in Computer Science  
doi:10.1007/3-540-36467-6_12 fatcat:esgj4pl7djahdovkep743rx75a

Deriving Private Information from Perturbed Data Using IQR Based Approach

Songtao Guo, Xintao Wu, Yingjiu Li
2006 22nd International Conference on Data Engineering Workshops (ICDEW'06)  
The experimental results show that current random perturbation-based privacy preserving data mining techniques may need a careful scrutiny in order to prevent privacy breaches through this model based  ...  inference.  ...  privacy interval specified by data owners.  ... 
doi:10.1109/icdew.2006.47 dblp:conf/icde/GuoWL06 fatcat:whae7hmshfhvlcyz2d3tjgt2l4

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

A Hasan, Qiang Qu, Chengming Li, Lifei Chen, Qingshan Jiang
2018 ISPRS International Journal of Geo-Information  
We propose a bounded perturbation method for anonymizing identified trajectories. Note that perturbation methods modify spatial coordinates by adding random noises [15, 21] .  ...  For instance, a trajectory would expose user interest in places and behaviors in time by inference and linking attacks.  ...  Conversely, perturbation modifies the location coordinates by adding some random noise. For example, random noise can be generated by a Gaussian or uniform distribution.  ... 
doi:10.3390/ijgi7020053 fatcat:wkrlkddfizgo5jr3bdrf6qyosi

Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials

Yingying Zhuang, Ying Huang, Peter B. Gilbert
2019 The International Journal of Biostatistics  
Motivated by randomized placebo-controlled preventive vaccine efficacy trials, within the principal stratification framework a pseudo-score type estimator has been proposed to estimate disease risks conditional  ...  We also propose a perturbation resampling method for making simultaneous inference on conditional vaccine efficacy over the values of the biomarker.  ...  Research reported in this publication was supported by Sanofi Pasteur and the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Department of Health and  ... 
doi:10.1515/ijb-2018-0058 pmid:31265429 pmcid:PMC6939148 fatcat:li5nx7ngirhy5a5g3ylbafl7se

On idiosyncratic stochasticity of financial leverage effects

Carles Bretó
2014 Statistics and Probability Letters  
We model leverage as stochastic but independent of return shocks and of volatility and perform likelihood-based inference via the recently developed iterated filtering algorithm using S&P500 data, contributing  ...  new evidence to the still slim empirical support for random leverage variation.  ...  Acknowledgments This work was supported by Spanish Government Project ECO2012-32401 and Spanish Program Juan de la Cierva (JCI-2010-06898).  ... 
doi:10.1016/j.spl.2014.04.003 fatcat:htfomdc24vfvblrof4wav5ykza

When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System [chapter]

Xiao Liu, An Liu, Xiangliang Zhang, Zhixu Li, Guanfeng Liu, Lei Zhao, Xiaofang Zhou
2017 Lecture Notes in Computer Science  
However, none is designed for both hiding users' private data and preventing privacy inference.  ...  We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed  ...  Research reported in this publication was partially supported by KAUST and Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61632016, 61402313).  ... 
doi:10.1007/978-3-319-55753-3_36 fatcat:o4s32n7ug5aedbhmellw3gpupm

Privacy Preserving Metering Protocol in Smart Grids [chapter]

Dilan Mert, Mehmet Ulvi Şimşek, Suat Özdemir
2015 IFIP Advances in Information and Communication Technology  
In the proposed protocol, consumption reports are generated by smart meters and sent to a data collection center using Laplace Distribution based time perturbation.  ...  In addition, SSS is employed to prevent attacks during multi-hop data transmission. The time perturbation prevents malicious users to see the actual data while it is stored in the data center.  ...  Before transmitting these measurement batches smart meters use a time based perturbation scheme to measured values to prevent time series analysis of data at the data collection server.  ... 
doi:10.1007/978-3-319-23868-5_34 fatcat:lhwi7nr6m5da7itqtyyo5gvnni

Privacy challenges and solutions in the social web

Grigorios Loukides, Aris Gkoulalas-Divanis
2009 XRDS Crossroads The ACM Magazine for Students  
His research interests are in the areas of privacy preserving data mining, privacy in medical records and privacy in location-based services.  ...  His research interests lie broadly in the fields of privacy and trust in data management and emerging database applications.  ...  Liu and Terzi have also proposed an algorithm based on random perturbation [10] .  ... 
doi:10.1145/1665997.1666002 fatcat:34qfpltjzfaxnblcr47e3cczhy

Output privacy in data mining

Ting Wang, Ling Liu
2011 ACM Transactions on Database Systems  
preventing the mining output (models or patterns) from malicious inference attacks.  ...  This paper presents a systematic study on the problem of protecting output privacy in data mining, and particularly, stream mining: (i) we highlight the importance of this problem by showing that even  ...  ACKNOWLEDGEMENTS This work is partially sponsored by grants from NSF CyberTrust, NSF NetSE, an IBM SUR grant, and a grant from Intel Research Council.  ... 
doi:10.1145/1929934.1929935 fatcat:hjxhddlkr5fcrdafcx4gyzgjcy

The Security of Confidential Numerical Data in Databases

Rathindra Sarathy, Krishnamurty Muralidhar
2002 Information systems research  
The methodology can also be used to evaluate the security provided by different security mechanisms such as query restrictions and data perturbation.  ...  When access to individual values of confidential numerical data in the database is prevented, disclosure may occur when a snooper uses linear models to predict individual values of confidential attributes  ...  random queries.  ... 
doi:10.1287/isre.13.4.389.74 fatcat:mwepqnbedve6niipl2mpmrpdau

DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications [article]

Changchang Liu and Supriyo Chakraborty and Prateek Mittal
2017 arXiv   pre-print
However, the same data can also be used by an adversary to make sensitive inferences about a user thereby violating her privacy.  ...  Personal sensory data is used by context-aware mobile applications to provide utility.  ...  Tradeoffs: We propose an effective perturbation mechanism which consists of two key techniques: (1) autoencoder based data minimization and (2) feature obfus-cation based data perturbation.  ... 
arXiv:1702.06159v2 fatcat:disdrn56irfzlaxjnjxmsmfpte

A large-scale neural network training framework for generalized estimation of single-trial population dynamics [article]

Mohammad Reza Keshtkaran, Andrew Robert Sedler, Raeed H Chowdhury, Raghav Tandon, Diya Basrai, Sarah L Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E Miller, Chethan Pandarinath
2021 bioRxiv   pre-print
This enables accurate inference of dynamics out-of-the-box on a variety of datasets, including data from M1 during stereotyped and free-paced reaching, somatosensory cortex during reaching with perturbations  ...  However, applying such methods to less-structured behaviors, or in brain areas that are not well-modeled by autonomous dynamics, is far more challenging, because deep learning methods often require careful  ...  Models are trained for fixed intervals (generations), between which poorly-performing models are replaced by copies of 114 better-performing models with perturbed HPs. 157 Population 157 Based Training  ... 
doi:10.1101/2021.01.13.426570 fatcat:pqtpseiavbfhlarl3n2xk2ssq4

A General Survey of Privacy-Preserving Data Mining Models and Algorithms [chapter]

Charu C. Aggarwal, Philip S. Yu
2008 Privacy-Preserving Data Mining  
We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining.  ...  We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.  ...  The key goal here is to prevent adversaries from making inferences from the end results of data mining and management applications.  ... 
doi:10.1007/978-0-387-70992-5_2 dblp:series/ads/AggarwalY08a fatcat:awhpyh3d2ndrrcecfzryy653uy

A Comprehensive Survey on Privacy Preserving Big Data Mining

S. Srijayanthi, R. Sethukkarasi
2017 International Journal of Computer Applications Technology and Research  
information from sensitive data.  ...  Privacy-Preserving Data Mining (PPDM) aids to mine information and reveals patterns from large dataset protecting private and sensitive data from being exposed.  ...  The basic idea of value-based perturbation approach is to add random noise to the data values.  ... 
doi:10.7753/ijcatr0602.1002 fatcat:yh2cedmfgbehfnt77opnacdxai

Butterfly: Protecting Output Privacy in Stream Mining

Ting Wang, Ling Liu
2008 2008 IEEE 24th International Conference on Data Engineering  
The latter refers to preventing the mining output (model/pattern) from malicious pattern-based inference attacks.  ...  Privacy preservation in data mining demands protecting both input and output privacy. The former refers to sanitizing the raw data itself before performing mining.  ...  ACKNOWLEDGMENT This research is partially sponsored by grants from NSF CyberTrust, an IBM SUR grant, and an IBM faculty award.  ... 
doi:10.1109/icde.2008.4497526 dblp:conf/icde/WangL08 fatcat:flw4m2t2nfgfxlz4jcqublizde
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