Fetching the paper…
Reading the bibliography…
Aggregate location data is often used to support smart services and applications, e.g., generating live traffic maps or predicting visits to businesses.
Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
Earlier work this paper cites.
Differential privacy: A survey of results
C. Dwork · 2008
Earlier work this paper cites.
Liblinear: A library for large linear classification
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 2008
Earlier work this paper cites.
Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
Earlier work this paper cites.
Privacy: Theory meets practice on the map
A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber · 2008
Earlier work this paper cites.
The bright side of sitting in traffic: Crowdsourcing road congestion data
D. Barth · 2009
Earlier work this paper cites.
On the anonymity of home/work location pairs
P. Golle and K. Partridge · 2009
Earlier work this paper cites.
CRAWDAD EPFL/Mobility Dataset
M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser · 2009
Earlier work this paper cites.
Learning your identity and disease from research papers: information leaks in genome wide association study
R. Wang, Y. F. Li, X. Wang, H. Tang, and X. Zhou · 2009
Earlier work this paper cites.
Differential privacy under continual observation
C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum · 2010
Earlier work this paper cites.
Differentially private aggregation of distributed time-series with transformation and encryption
V. Rastogi and S. Nath · 2010
Earlier work this paper cites.
Unraveling an old cloak: k-anonymity for location privacy
R. Shokri, C. Troncoso, C. Diaz, J. Freudiger, and J.-P. Hubaux · 2010
Earlier work this paper cites.
Private and continual release of statistics
T.-H. H. Chan, E. Shi, and D. Song · 2011
Earlier work this paper cites.
Privacy and Accountability for Location-based Aggregate Statistics
R. A. Popa, A. J. Blumberg, H. Balakrishnan, and F. H. Li · 2011
Cited alongside, same era.
Quantifying location privacy: the case of sporadic location exposure
R. Shokri, G. Theodorakopoulos, G. Danezis, J.-P. Hubaux, and J.-Y. Le Boudec · 2011
Cited alongside, same era.
Anonymization of location data does not work: A large-scale measurement study
H. Zang and J. Bolot · 2011
Cited alongside, same era.
Avoiding the crowds: understanding tube station congestion patterns from trip data
I. Ceapa, C. Smith, and L. Capra · 2012
Cited alongside, same era.
Quantifying and protecting location privacy
R. Shokri · 2012
Cited alongside, same era.
Unique in the Crowd: The privacy bounds of human mobility
Predicting traffic volumes and estimating the effects of shocks in massive transportation systems
R. Silva, S. M. Kang, and E. M. Airoldi · 2015
Later among the works it cites.
Membership privacy in MicroRNA-based studies
M. Backes, P. Berrang, M. Humbert, and P. Manoharan · 2016
Later among the works it cites.
Differentially private publication of location entropy
H. To, K. Nguyen, and C. Shahabi · 2016
Later among the works it cites.
Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering
N. Buscher, S. Boukoros, S. Bauregger, and S. Katzenbeisser · 2017
Closest in time.
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2017
Closest in time.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y.-A. de Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel · 2013
Cited alongside, same era.
Membership privacy: a unifying framework for privacy definitions
N. Li, W. Qardaji, D. Su, Y. Wu, and W. Yang · 2013
Cited alongside, same era.
The NHGRI GWAS Catalog, a Curated Resource of SNP-Trait Associations
D. Welter, J. MacArthur, J. Morales, T. Burdett, P. Hall, H. Junkins, A. Klemm, P. Flicek, T. Manolio, L. Hindorff, et al · 2013
Cited alongside, same era.
A case study: Privacy-preserving release of spatio-temporal density in Paris
G. Acs and C. Castelluccia · 2014
Cited alongside, same era.
Differentially private event sequences over infinite streams
G. Kellaris, S. Papadopoulos, X. Xiao, and D. Papadias · 2014
Cited alongside, same era.
It’s the way you check-in: Identifying users in Location-based Social Networks
L. Rossi and M. Musolesi · 2014
Cited alongside, same era.
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge
S. Ji, W. Li, N. Z. Gong, P. Mittal, and R. A. Beyah · 2015
Cited alongside, same era.
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
Closest in time.
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2017
Closest in time.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Closest in time.
https://www.business-solutions.telefonica.com/en/enterprise/solutions/smarter-selling/big-data-insights/ , 2017
Telefonica Smart Steps · 2017
Closest in time.
Emoji frequency detection and deep link frequency
A. G. Thakurta, A. H. Vyrros, U. S. Vaishampayan, G. Kapoor, J. Freudinger, V. V. Prakash, A. Legendre, and S. Duplinsky · 2017
Closest in time.
https://www.waze.com , 2017
Waze · 2017
Closest in time.
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data
F. Xu, Z. Tu, Y. Li, P. Zhang, X. Fu, and D. Jin · 2017
Closest in time.