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Practical privacy

Avrim Blum, Cynthia Dwork, Frank McSherry, Kobbi Nissim
2005 Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '05  
First, we modify the privacy analysis to real-valued functions f . Second, we examine the computational power of the SuLQ primitive.  ...  We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.  ...  We note that whereas in prior work privacy had to be proved 'from scratch', in the SuLQ framework privacy follows by virtue of working within the framework.  ... 
doi:10.1145/1065167.1065184 dblp:conf/pods/BlumDMN05 fatcat:jl6nbcazebfadczovnk3qnhk2a

Practical Privacy: The SuLQ Framework

Avrim Blum, Cynthia Dwork, Frank McSherry, Kobbi Nissim
2018
Second, we examine the computational power of the SuLQ primitive.  ...  We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive.  ...  We note that whereas in prior work privacy had to be proved 'from scratch', in the SuLQ framework privacy follows by virtue of working within the framework.  ... 
doi:10.1184/r1/6608516 fatcat:a7zgsgs2xrcntlfowqealb2efi

Differential Privacy in Practice

Hiep H. Nguyen, Jong Kim, Yoonho Kim
2013 Journal of Computing Science and Engineering  
We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk  ...  It has born fruitful results over the past ten years, both in theoretical connections to other fields and in practical applications to real-life datasets.  ...  ACKNOWLEDGMENTS This research was supported by World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea (R31-10100).  ... 
doi:10.5626/jcse.2013.7.3.177 fatcat:xrcbyxpzfvh2tfdrr5572rdnku

Privacy: Theory meets Practice on the Map

Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, Lars Vilhuber
2008 2008 IEEE 24th International Conference on Data Engineering  
Census Bureau, but due to privacy constraints, they cannot be used directly by the mapping program.  ...  In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community.  ...  One way to do this is to cluster the data using a privacy-preserving clustering algorithm that is compatible with differential privacy, such as the k-means algorithm in the SULQ framework [19] .  ... 
doi:10.1109/icde.2008.4497436 dblp:conf/icde/MachanavajjhalaKAGV08 fatcat:neqxrlbfs5di3czfak3qyzizya

Data mining with differential privacy

Arik Friedman, Assaf Schuster
2010 Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10  
We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework.  ...  The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy.  ...  It is based on a theoretical algorithm that was presented in the SuLQ framework (Sub-Linear Queries [1] ), a predecessor of differential privacy, so we will refer to it as SuLQ-based ID3.  ... 
doi:10.1145/1835804.1835868 dblp:conf/kdd/FriedmanS10 fatcat:74lifdodcfgw7l52vbwc3muqby

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems

Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu
2018 International Conference on Artificial Intelligence and Statistics  
In a distributed optimization framework, data providers can use this algorithm to collaboratively analyze the union of their data sets while limiting the disclosure of their private information.  ...  Our algorithm provides fine-tuned control over the tradeoff between estimation accuracy and privacy preservation.  ...  Since the Gibbs sampling only approximates the matrix Bingham distribution, the ✏-differential privacy is not guaranteed by PPCA in practice.  ... 
dblp:conf/aistats/GeWWL18 fatcat:bobp3x5u5vcurol6vsdmp3ukpe

Differential Privacy Principal Component Analysis for Support Vector Machines

Yuxian Huang, Geng Yang, Yahong Xu, Hao Zhou, A. Peinado
2021 Security and Communication Networks  
Specifically, we introduced differential privacy mechanisms at different stages of the algorithm PCA-SVM and obtained the algorithms DPPCA-SVM and PCADP-SVM.  ...  In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data.  ...  Jiangsu Province (KYCX180891), the Natural Science Foundation of Jiangsu Province (BK20161516 and BK20160916), and the Postdoctoral Science Foundation Project of China (2016M601859).  ... 
doi:10.1155/2021/5542283 fatcat:rvjxzryvpja5zkjz2xyn3vayzq

Epistemic privacy

Alexandre Evfimievski, Ronald Fagin, David P. Woodruff
2008 Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '08  
We present a novel definition of privacy in the framework of offline (retroactive) database query auditing.  ...  Nevertheless, for many interesting families, such as the family of product distributions, we obtain algorithms that are efficient both in theory and in practice.  ...  Acknowledgements: We thank Kenneth Clarkson for bringing our attention to the Four Functions Theorem.  ... 
doi:10.1145/1376916.1376941 dblp:conf/pods/EvfimievskiFW08 fatcat:chdq77jgbvgfrdh756wzclglfm

Epistemic privacy

Alexandre Evfimievski, Ronald Fagin, David Woodruff
2010 Journal of the ACM  
We present a novel definition of privacy in the framework of offline (retroactive) database query auditing.  ...  Nevertheless, for many interesting families, such as the family of product distributions, we obtain algorithms that are efficient both in theory and in practice.  ...  Acknowledgements: We thank Kenneth Clarkson for bringing our attention to the Four Functions Theorem.  ... 
doi:10.1145/1870103.1870105 fatcat:m5f6dpodonejlkfnrq2gbeadpy

Blowfish privacy

Xi He, Ashwin Machanavajjhala, Bolin Ding
2014 Proceedings of the 2014 ACM SIGMOD international conference on Management of data - SIGMOD '14  
In this paper, we present Blowfish, a class of privacy definitions inspired by the Pufferfish framework, that provides a rich interface for this trade-off.  ...  Privacy definitions provide ways for trading-off the privacy of individuals in a statistical database for the utility of downstream analysis of the data.  ...  Acknowledgements: We would like to thank Jiangwei Pan and the anonymous reviewers for their comments.  ... 
doi:10.1145/2588555.2588581 dblp:conf/sigmod/HeMD14 fatcat:v7cbq7dot5ccrjbztaipivp4n4

Privacy-Preserving Classifier Learning [chapter]

Justin Brickell, Vitaly Shmatikov
2009 Lecture Notes in Computer Science  
We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers.  ...  We evaluate a prototype implementation, and demonstrate that its performance is efficient for practical scenarios.  ...  This paper is based upon work supported by the NSF grants IIS-0534198 and CNS-0615104, and the ARO grant W911NF-06-1-0316.  ... 
doi:10.1007/978-3-642-03549-4_8 fatcat:ljlgtzlbtvfhvcc6o2ry5jbnlm

Privacy via pseudorandom sketches

Nina Mishra, Mark Sandler
2006 Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems - PODS '06  
The sketches guarantee that each individual's privacy is provably maintained assuming one of the strongest definitions of privacy that we are aware of: given unlimited computational power and arbitrary  ...  Existing techniques for perturbing data are limited by the number of users required to obtain approximate answers to queries, the richness of preserved statistical behavior, the privacy guarantees given  ...  While this is a very nice and appealing framework -since no data is changed, only supressed -it has been shown that it might lead to privacy breaches in some applications.  ... 
doi:10.1145/1142351.1142373 dblp:conf/pods/MishraS06 fatcat:o5ehjmpbfbakvawxdrk5vjzj4u

Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels [article]

Li Chen
2019 arXiv   pre-print
However when the graph contains private or sensitive information, treating the data as non-private can potentially leak privacy and incur disclosure risks.  ...  privacy parameters in simulated and real world networks.  ...  The proof of Theorem 1 follows directly from the differentially private algorithm SULQ to approximate PCA with privacy guarantee (Blum et al., 2005; Chaudhuri et al., 2012) .  ... 
arXiv:1905.07065v1 fatcat:w2pevf2yq5arndetxhlc6fozxu

When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection [article]

Xiangyun Tang, Liehuang Zhu, Meng Shen, Xiaojiang Du
2018 arXiv   pre-print
Extensive experimental results show the effectiveness and efficiency of the proposed scheme.  ...  By combining homomorphic cryptosystem (HC) with differential privacy (DP), Heda obtains the tradeoffs between efficiency and accuracy, and enables flexible switch among different tradeoffs by parameter  ...  In the future work, we plan to explore a generalized framework which enables constructing a wide range of privacy-preserving ML classifier training algorithms.  ... 
arXiv:1812.02292v1 fatcat:34hraoj3gncbvihyjwxas4omhq

A Survey of Cryptographic and Non-cryptographic Techniques for Privacy Preservation

Bhawani Singh, Anju Singh, Divakar Singh
2015 International Journal of Computer Applications  
The cryptography techniques are implemented to offer a tough security and privacy and also offer exact and practical implementation.  ...   It takes long to create the code.  work). That approach is referred as single-attribute SuLQ databases.  ... 
doi:10.5120/ijca2015907146 fatcat:cjecbbvnjff43io7zz5adg2by4
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