4,171 Hits in 6.9 sec

Limiting Attribute Disclosure in Randomization Based Microdata Release

Ling Guo, Xiaowei Ying, Xintao Wu
2011 Journal of Computing Science and Engineering  
We give efficient solutions to determine optimal distortion parameters, such that we can maximize utility preservation while still satisfying privacy requirements.  ...  We compare our randomization approach with l-diversity and anatomy in terms of utility preservation (under the same privacy requirements) from three aspects (reconstructed distributions, accuracy of answering  ...  That is, analysis on contingency tables is equivalent to analysis on the original categorical data. Table 1b shows one contingency table instance derived from the data set with 100 tuples.  ... 
doi:10.5626/jcse.2011.5.3.169 fatcat:jjhmgpy2sfch5ndihwso6ty6py

A Comprehensive Survey on Local Differential Privacy

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu
2020 Security and Communication Networks  
Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while  ...  With the advent of the era of big data, privacy issues have been becoming a hot topic in public.  ...  Ordinal data are the categorical data with linear ordering among categories, including the discrete numerical data (such as discrete sensor data) and other categorical data (such as preference options)  ... 
doi:10.1155/2020/8829523 fatcat:xjk3vgyambb5xioc2q5hyr2hua

A comprehensive review on privacy preserving data mining

Yousra Abdul Alsahib S. Aldeen, Mazleena Salleh, Mohammad Abdur Razzaque
2015 SpringerPlus  
The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining  ...  in terms of data analysis, validation, and publishing.  ...  neighbour and the categorization of privacy preserving.  ... 
doi:10.1186/s40064-015-1481-x pmid:26587362 pmcid:PMC4643068 fatcat:twwirrmehva4pfkieiufldrxve

Cloning for privacy protection in multiple independent data publications

Muzammil M. Baig, Jiuyong Li, Jixue Liu, Hua Wang
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves good data utility.  ...  Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data owner.  ...  Partition-based privacy preserving data publishing techniques address this problem by anonymizing data such that individual privacy is preserved when data is shared or released.  ... 
doi:10.1145/2063576.2063705 dblp:conf/cikm/BaigLLW11 fatcat:sqqpydjs4vd4dhuatljbt4gopm

Distortion Based Algorithms for Privacy Preserving Frequent Item Set Mining

K Srinivasa Rao, V Chiranjeevi
2011 International Journal of Data Mining & Knowledge Management Process  
The normal distortion procedure does not provide the flexibility of tuning the probability parameters for balancing privacy and accuracy parameters, and each item's presence/absence is modified with an  ...  In order to preserve the privacy of the client in data mining process, a variety of techniques based on random perturbation of data records have been proposed recently.  ...  Evmievski et al. 2002) proposed a framework for mining association rules from transactions consisting of categorical items in which the data has been randomized to preserve privacy of individual transactions  ... 
doi:10.5121/ijdkp.2011.1402 fatcat:nquyw2ihmnhezptg6fqshvti6q

Survey on Privacy-Preserving Techniques for Data Publishing [article]

Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes
2022 arXiv   pre-print
However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance.  ...  Such de-identification is guaranteed through privacy-preserving techniques.  ...  We stress the relevance in reproducing experimental studies in regression tasks and for clustering analysis as the end use of data is often unknown.  ... 
arXiv:2201.08120v1 fatcat:d7jy6jnuwbftpnahf4ywklztje

The importance of tracing data through the visualization pipeline

Aritra Dasgupta, Robert Kosara
2012 Proceedings of the 2012 BELIV Workshop on Beyond Time and Errors - Novel Evaluation Methods for Visualization - BELIV '12  
By feeding that information back into the pipeline, visualization systems will be able to adapt the display to the data to be shown, the parameters of the output device, and even the user.  ...  Visualization research focuses either on the transformation steps necessary to create a visualization from data, or on the perception of structures after they have been shown on the screen.  ...  of web data for privacy-preserving manifold visualization [25] .  ... 
doi:10.1145/2442576.2442585 dblp:conf/beliv/DasguptaK12 fatcat:wrkw5tvyynhnxcmipurm336aee

A Case Study on Mining Security Issues & Remedies in Privacy Preservation

Lakkam Ravi Kumar
2017 International Journal for Research in Applied Science and Engineering Technology  
We'd like to provide useful insights into the study of privacy preserving data mining.  ...  The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data.  ...  CONCLUSION This paper presented a privacy preserving technique that adds noise to each and every at-tribute, both numerical and categorical, of a data set.  ... 
doi:10.22214/ijraset.2017.11105 fatcat:awtbkad3cncavo2rbku7drtxwy

Mining Association Rules under Privacy Constraints [chapter]

Jayant R. Haritsa
2008 Privacy-Preserving Data Mining  
We analyze the various schemes that have been proposed for this purpose with regard to a variety of parameters including the degree of trust, privacy metric, model accuracy and mining efficiency.  ...  Data mining services require accurate input data for their results to be meaningful, but privacy concerns may impel users to provide spurious information.  ...  An alternative approach for output rule privacy proposed in [37, 36] is to use the concept of "data blocking", wherein some values in the database are replaced with NULLs signifying unknowns.  ... 
doi:10.1007/978-0-387-70992-5_10 dblp:series/ads/Haritsa08 fatcat:q7b7p3fk2jahfpmzygqnb3czva

Literature Survey of Association Rule Based Techniques for Preserving Privacy

Shrey Agrawal
2016 Figshare  
The paper gives an overview of privacy preserving in association rule mining techniques. In this paper, all the present privacy preserving using association rule hiding techniques are discussed.  ...  A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy.  ...  It is thus important to provide users with a set of metrics which will enable them to select the most appropriate privacy preserving technique for the data at hand; with respect to some specific parameters  ... 
doi:10.6084/m9.figshare.3412345 fatcat:kdyn4ykpwzc53fmfi4prs3p5u4

FRAPP: a framework for high-accuracy privacy-preserving mining

Shipra Agrawal, Jayant R. Haritsa, B. Aditya Prakash
2008 Data mining and knowledge discovery  
To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently.  ...  The quantitative utility of FRAPP, which is a general-purpose random-perturbation-based privacy-preserving mining technique, is evaluated specifically with regard to association and classification rule  ...  This input can be produced through the "ByClass" privacy-preserving algorithm enunciated by Agrawal and Srikant (2000) , which partitions the training data by class label, and then separately distorts  ... 
doi:10.1007/s10618-008-0119-9 fatcat:2p67g5fw4nbdxp436j2l2od4l4

Fuzzy data distortion

S.K. Panda, A. Nagabhushanam
1995 Computational Statistics & Data Analysis  
Fuzzy approach has been exploited and successfully tested for data distortion to achieve disclosure control and data security in patient record statistical databases of fifty thousand monthly records in  ...  We put forward a strong case to recommend that the new method explained in this technical paper yields better results compared to probability and value distortion in preserving order&moment statistics  ...  The same method can also be applied to categorical data.  ... 
doi:10.1016/0167-9473(94)00015-b fatcat:ul7dwfwf5bfa7gtyvjwzfwdfny

Third Party Privacy Preserving Protocol for Perturbation Based Classification of Vertically Fragmented Data Bases [article]

B.Hanmanthu, B.Raghu Ram, P.Niranjan
2013 arXiv   pre-print
Privacy is become major issue in distributed data mining.  ...  In the literature we can found many proposals of privacy preserving which can be divided into two major categories that is trusted third party and multiparty based privacy protocols.  ...  Then, an effective Naive Bayes classifier is presented to predict the class labels for unknown samples according to the distorted data by RRPH.  ... 
arXiv:1304.6575v1 fatcat:wlxxpa4zmffyhajfp24cjcpxgq

Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

Sk. Mohammed Gouse, G.Krishna Mohan
2020 International Journal of Advanced Computer Science and Applications  
As a result, a novel framework is required to improve the privacy as well as accuracy on the high dimensional privacy preserving data with less runtime.  ...  A hybrid machine learning classifier is proposed to predict the privacy preserving class label based on the training data.  ...  Data understanding is important and is combined with the need to use appropriate algorithms to preserve privacy.  ... 
doi:10.14569/ijacsa.2020.0111034 fatcat:ckipn7ikhvhrtc35rggyhe5yvi

Adversarial representation learning for synthetic replacement of private attributes [article]

John Martinsson, Edvin Listo Zec, Daniel Gillblad, Olof Mogren
2021 arXiv   pre-print
with an independent random sample.  ...  Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing  ...  PRIVACY-PRESERVING ADVERSARIAL REPRESENTATION LEARNING In the current work, we focus on utility-preserving transformations of data: we use privacy-preserving representation learning to obfuscate information  ... 
arXiv:2006.08039v5 fatcat:sciapl65z5dwfpoqo3pu4gxi5q
« Previous Showing results 1 — 15 out of 4,171 results