Pan-private algorithms via statistics on sketches
By Darakhshan J. Mir, S. Muthu Muthukrishnan, Aleksandar Nikolov, Rebecca N. Wright. 2011
99 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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
- Ninghui Li
- Somesh Jha
- Tianhao Wang
- Aleksandra Korolova
- Rasmus Pagh
- Jonathan Ullman
- Matthew Joseph
- Albert Cheu
In Computational Theory and Mathematics
- Elaine Shi
- Aleksandra Korolova
- Xiaokui Xiao
- Albert Cheu
- Victor Balcer
- T-H. Hubert Chan
- Matthew Joseph
- Georgios Kellaris
Citations in the corpus
- RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response (2014, background): “Usage of various dimensionality reduction techniques in order to improve the privacy properties of algorithms while retaining utility is also fairly common [20, 17, 1, 22].”
- Distributed Private Heavy Hitters (2012, background): “Also, [DNP + 10, MMNW11] work in the centralized setting, but demand pan-privacy , which roughly requires that the internal state of the algorithm itself remain differentially private.”
- Connecting Robust Shuffle Privacy and Pan-Privacy (2020, background): “To do so, we give a way to transform a robustly shuffle private protocol into a pan-private one (Algorithm 3) and then invoke a lower bound for pan-private distinct elements [29].”
- Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams (2012, background): “[18] also considered the problem of private streaming algorithms to return the counts of heavy hitters, not the heavy hitters themselves.”
- Locally Differentially Private Heavy Hitter Identification (2017, background): “The problem has been studied in the centralized DP setting [7], [24].”
- Pan-Private Uniformity Testing (2019, background): “Finally, we note that these results add to lines of work conceptually connecting restricted memory to pan-privacy [25, 36] and connecting restricted communication to local privacy [33, 1, 21, 31].”
- Differential Privacy (2015, background): “One approach to privacy on streaming data is Pan-privacy [18], a stronger notion than differential privacy which ensures that the entire state of the sys-tem is private.”
- Differentially Private Event Sequences over Infinite Streams (2014, background): “Mir et al. [27] consider counters that may also decrease.”
- Efficient Differentially Private F0 Linear Sketching (2020, methodology): “However, the sensitivity analysis in that paper has an error [39] that was corrected in the slower method published in [35].)”