Access the full text.
Sign up today, get DeepDyve free for 14 days.
(2018)
United States Cities Database
Resistant to post-processing: If M : N N × Q (cid:32) Y is d X -private, and f : Y → Y 0 is any arbitrary (randomized) function, then the composition f ◦M : N N × Q (cid:32) Y 0 is also d X -private
Arpita Ghosh, Aaron Roth (2010)
Selling privacy at auction
S. Fienberg, A. Rinaldo, Xiaolin Yang (2010)
Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables
S. Vadhan (2017)
The Complexity of Differential Privacy
Zach Jorgensen, Ting Yu, Graham Cormode (2015)
Conservative or liberal? Personalized differential privacy2015 IEEE 31st International Conference on Data Engineering
Xi He, Ashwin Machanavajjhala, Bolin Ding (2013)
Blowfish privacy: tuning privacy-utility trade-offs using policiesProceedings of the 2014 ACM SIGMOD International Conference on Management of Data
Chao Li, G. Miklau (2011)
Efficient Batch Query Answering Under Differential PrivacyArXiv, abs/1103.1367
Moritz Hardt, Katrina Ligett, Frank McSherry (2010)
A Simple and Practical Algorithm for Differentially Private Data ReleaseArXiv, abs/1012.4763
M. Andrés, N. Bordenabe, K. Chatzikokolakis, C. Palamidessi (2012)
Geo-indistinguishability: differential privacy for location-based systemsProceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Moritz Hardt, Kunal Talwar (2009)
On the geometry of differential privacyArXiv, abs/0907.3754
H. Mehta, Pratik Kanani, Priya Lande (2019)
Google MapsInternational Journal of Computer Applications
C. Dwork, Aaron Roth (2014)
The Algorithmic Foundations of Differential PrivacyFound. Trends Theor. Comput. Sci., 9
K. Chatzikokolakis, M. Andrés, N. Bordenabe, C. Palamidessi (2013)
Broadening the Scope of Differential Privacy Using Metrics
c ○ 2012 Society for Industrial and Applied Mathematics UNIVERSALLY UTILITY-MAXIMIZING PRIVACY MECHANISMS ∗
B. Barak, Kamalika Chaudhuri, C. Dwork, Satyen Kale, Frank McSherry, Kunal Talwar (2007)
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Samuel Haney, Ashwin Machanavajjhala, Bolin Ding (2014)
Design of Policy-Aware Differentially Private AlgorithmsProc. VLDB Endow., 9
Mohammad Alaggan, S. Gambs, Anne-Marie Kermarrec (2015)
Heterogeneous Differential PrivacyArXiv, abs/1504.06998
Avrim Blum, Katrina Ligett, Aaron Roth (2013)
A learning theory approach to noninteractive database privacyJ. ACM, 60
Stephen Wright (2015)
Coordinate descent algorithmsMathematical Programming, 151
Avrim Blum, C. Dwork, Frank McSherry, Kobbi Nissim (2005)
Practical privacy: the SuLQ framework
Yangyang Xu, W. Yin (2013)
A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and CompletionSIAM J. Imaging Sci., 6
Chao Li, G. Miklau (2012)
An Adaptive Mechanism for Accurate Query Answering under Differential PrivacyProc. VLDB Endow., 5
Strategy 2: c k = max i,j k Q : ,i − Q : ,j k 1 d X ( i,j ) , ∀ k ∈ [ K ] , i.e.
C. Dwork, Frank McSherry, Kobbi Nissim, Adam Smith (2006)
Calibrating Noise to Sensitivity in Private Data Analysis
C. Dwork, Moritz Hardt, T. Pitassi, Omer Reingold, R. Zemel (2011)
Fairness through awarenessArXiv, abs/1104.3913
Frank McSherry, Kunal Talwar (2007)
Mechanism Design via Differential Privacy48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07)
AbstractDifferential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider dX -privacy, an instantiation of the privacy notion introduced in [6], which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its dX -private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several dX -private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed dX -private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.
Proceedings on Privacy Enhancing Technologies – de Gruyter
Published: Jan 1, 2020
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.