Differentially private summaries for sparse data
By Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Thanh T. L. Tran. 2012
115 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aleksandra Korolova
- Zachary Chase Lipton
- Emiliano De Cristofaro
- Krishnaram Kenthapadi
- Ilya Mironov
- Luca Melis
- Vitaly Shmatikov
- Rasmus Pagh
In Management Science and Operations Research
- Nina Mishra
- Ramón Cáceres
- Luca Melis
- Ilya Mironov
- Rebecca N. Wright
- Margaret Martonosi
- George Danezis
- Charles Peter Elkan
Citations in the corpus
- Efficient Private Statistics with Succinct Sketches (2015, background): “Other proposals combine differential privacy and Count-Min Sketch to obtain aggregate information about vehicle traffic [53] as well as summaries of sparse databases [23].”
- Privacy via the Johnson-Lindenstrauss Transform (2012, background): “The high-level idea of using a condensed representation of the input obtained via some randomized transformation in order to protect privacy while preserving the utility of some computations has been explored by [38, 46, 29, 45, 32, 12].”
- DP-WHERE: Differentially private modeling of human mobility (2013, methodology): “called dpmedian and is computed using a computationally efficient version of the exponential mechanism [26], as in [5].”
- Differential Privacy and Machine Learning: a Survey and Review (2014, background): “Many differentially private data release mechanisms have been described, such as [3, 1, 18, 10].”