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[1]
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A. Biswas, G. Cormode, Y. Kanza, and Z. Zhou.
Differentially private hierarchical heavy hitters.
In ACM Principles of Database Systems (PODS), 2025.
|
|
[2]
|
G. Cormode, S. Maddock, and C. Maple.
FLAIM: AIM-based synthetic data generation in the federated
setting.
In ACM SIGKDD Conference, 2024.
|
|
[3]
|
G. Cormode, I. L. Markov, and H. Srinivas.
Private and efficient federated numerical aggregation.
In International Conference on Extending Database Technology,
pages 734-742, 2024.
|
|
[4]
|
W. Chen, G. Cormode, A. Bharadwaj, P. Romov, and A. Özgür.
Federated experiment design under distributed differential privacy.
In AISTATS, volume 238 of Proceedings of Machine Learning
Research, pages 2458-2466. PMLR, 2024.
|
|
[5]
|
G. Cormode, M. Dall'Agnol, T. Gur, and C. Hickey.
Streaming zero-knowledge proofs.
In Computational Complexity Conference (CCC), 2024.
|
|
[6]
|
G. Cormode and I. L. Markov.
Federated calibration and evaluation of binary classifiers.
In Proceedings of the VLDB Endowment, volume 16, pages
3253-3265, 2023.
|
|
[7]
|
W.-N. Chen, A. Özgür, G. Cormode, and A. Bharadwaj.
The communication cost of security and privacy in federated frequency
estimation.
In AISTATS, pages 4247-4274, 2023.
|
|
[8]
|
J. Hehir, D. Ting, and G. Cormode.
Sketch-flip-merge: Mergeable sketches for private distinct counting.
In International Conference on Machine Learning, (ICML),
2023.
|
|
[9]
|
A. Biswas and G. Cormode.
Interactive proofs for differentially private counting.
In ACM Conference on Computer and Communications Security,
2023.
|
|
[10]
|
K. Cai, X. Xiao, and G. Cormode.
Privlava: Synthesizing relational data with foreign keys under
differential privacy.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2023.
|
|
[11]
|
M. Shekelyan, G. Cormode, P. Triantafillou, Q. Ma, and A. M. Shanghooshabad.
Streaming weighted sampling over join queries.
In EDBT, pages 298-310, 2023.
|
|
[12]
|
K. Prasad, S. Ghosh, G. Cormode, I. Mironov, A. Yousefpour, and P. Stock.
Reconciling security and communication efficiency in federated
learning.
In International Workshop on Federated Learning: Recent Advances
and New Challenges in Conjunction with NeurIPS 2022, 2022.
|
|
[13]
|
S. Maddock, G. Cormode, S. Jha, C. Maple, and T. Wang.
Federated boosted decision trees with differential privacy.
In ACM Conference on Computer and Communications Security,
2022.
|
|
[14]
|
L. Watson, C. Guo, G. Cormode, and A. Sablayrolles.
On the importance of difficulty calibration in membership inference
attacks.
In International Conference on Learning Representations,
ICLR, 2022.
|
|
[15]
|
A. Bharadwaj and G. Cormode.
Sample-and-threshold differential privacy: Histograms and
applications.
In AISTATS, 2022.
(full version).
|
|
[16]
|
Z. Huang, Y. Qiu, K. Yi, and G. Cormode.
Frequency estimation under multiparty differential privacy: One-shot
and streaming.
In International Conference on Very Large Data Bases (VLDB),
volume 15, page 2058–2070. VLDB Endowment, 2022.
|
|
[17]
|
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek,
J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, G. Cormode, and I. Mironov.
Opacus: User-friendly differential privacy library in pytorch.
In Privacy in Machine Learning (NeurIPS workshop), 2021.
|
|
[18]
|
A. Bharadwaj and G. Cormode.
Sample-and-threshold differential privacy: Histograms and
applications.
In Privacy in Machine Learning (NeurIPS workshop), 2021.
(workshop version).
|
|
[19]
|
G. Cormode and I. Markov.
Bit-efficient numerical aggregation and stronger privacy for trust in
federated analytics.
In PPML Workshop, 2021.
|
|
[20]
|
G. Cormode, C. Maple, and M. Scott.
Applying the shuffle model of differential privacy to vector
aggregation.
In BICOD, 2021.
|
|
[21]
|
T. Cunningham, G. Cormode, H. Ferhatosmanoglu, and D. Srivastava.
Real-world trajectory sharing with local differential privacy.
In International Conference on Very Large Data Bases (VLDB),
2021.
|
|
[22]
|
G. Cormode, S. Maddock, and C. Maple.
Frequency estimation under local differential privacy.
In International Conference on Very Large Data Bases (VLDB),
2021.
|
|
[23]
|
T. Cunningham, G. Cormode, and H. Ferhatosmanoglu.
Privacy-preserving synthetic location data in the real world.
In Proceedings of International Symposium on Spatial and
Temporal Databases, 2021.
|
|
[24]
|
G. Cormode, A. Mishra, J. Ross, and P. Veselý.
Theory meets practice at the median:a worst case comparison of
relative error quantile algorithms.
In ACM SIGKDD Conference, 2021.
|
|
[25]
|
M. Shekelyan and G. Cormode.
Sequential random sampling revisited: Hidden shuffle method.
In AISTATS, volume 130 of Proceedings of Machine Learning
Research, pages 3628-3636. PMLR, 2021.
|
|
[26]
|
G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and P. Veselý.
Relative error streaming quantiles.
In ACM Principles of Database Systems (PODS), 2021.
|
|
[27]
|
G. Cormode, C. Dickens, and D. Woodruff.
Subspace exploration: Bounds on projected frequency estimation.
In ACM Principles of Database Systems (PODS), 2021.
|
|
[28]
|
G. Cormode, M. Garofalakis, and M. Shekelyan.
Data-independent space partitionings for summaries.
In ACM Principles of Database Systems (PODS), 2021.
|
|
[29]
|
G. Cormode and P. Veselý.
A tight lower bound for comparison-based quantile summaries.
In ACM Principles of Database Systems (PODS), pages 81-93.
ACM, 2020.
|
|
[30]
|
G. Cormode and C. Dickens.
Iterative hessian sketch in input sparsity time.
In Proceedings of Beyond First Order Methods in ML (NeurIPS
workshop), 2019.
|
|
[31]
|
G. Cormode and C. Hickey.
Efficient interactive proofs for linear algebra.
In Proceedings of International Symposium on Algorithms and
Computation (ISAAC), 2019.
|
|
[32]
|
G. Cormode and P. Veselý.
Streaming algorithms for bin packing and vector scheduling.
In Workshop on Approximation and Online Algorithms, 2019.
|
|
[33]
|
R. Chitnis and G. Cormode.
Towards a theory of parameterized streaming algorithms.
In International Symposium on Parameterized and Exact
Computation, 2019.
|
|
[34]
|
G. Cormode, T. Kulkarni, and D. Srivastava.
Answering range queries under local differential privacy.
In International Conference on Very Large Data Bases (VLDB),
2019.
|
|
[35]
|
G. Cormode, J. Dark, and C. Konrad.
Independent sets in vertex-arrival streams.
In International Colloquium on Automata, Languages and
Programming (ICALP), 2019.
|
|
[36]
|
G. Cormode, C. Dickens, and D. P. Woodruff.
Leveraging well-conditioned bases: Streaming and distributed
summaries in minkowski p-norms.
In International Conference on Machine Learning, (ICML),
2018.
|
|
[37]
|
G. Cormode, T. Kulkarni, and D. Srivastava.
Marginal release under local differential privacy.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2018.
|
|
[38]
|
G. Cormode and C. Hickey.
You can check others' work more quickly than doing it yourself.
In International Conference on Data Engineering (ICDE), 2018.
|
|
[39]
|
G. Cormode, J. Dark, and C. Konrad.
Approximating the caro-wei bound for independent sets in graph
streams.
In International Symposium on Combinatorial Optimization, 2018.
|
|
[40]
|
Y. Zhang, S. Tirthapura, and G. Cormode.
Learning graphical models from a distributed stream.
In International Conference on Data Engineering (ICDE), 2018.
|
|
[41]
|
G. Cormode and J. Dark.
Fast sketch-based recovery of correlation outliers.
In International Conference on Database Theory, 2018.
|
|
[42]
|
G. Cormode and C. Hickey.
Cheap checking for cloud computing: Statistical analysis via
annotated data streams.
In AISTATS, 2018.
|
|
[43]
|
G. Cormode, T. Kulkarni, and D. Srivastava.
Constrained private mechanisms for count data.
In International Conference on Data Engineering (ICDE), 2018.
|
|
[44]
|
G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan.
Streaming algorithms for matching size estimation in sparse graphs.
In European Symposium on Algorithms, 2017.
|
|
[45]
|
Z. Jorgensen, T. Yu, and G. Cormode.
Publishing attributed social graphs with formal privacy guarantees.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), pages 107-122, 2016.
|
|
[46]
|
R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, A. McGregor,
M. Monemizadeh, and S. Vorotnikova.
Kernelization via sampling with applications to dynamic graph
streams.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2016.
|
|
[47]
|
K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth.
Correlation clustering in data streams.
In International Conference on Machine Learning, (ICML),
pages 2237-2246, 2015.
|
|
[48]
|
X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava.
DPT: differentially private trajectory synthesis using hierarchical
reference systems.
In Proceedings of the VLDB Endowment, volume 8, pages
1154-1165, 2015.
|
|
[49]
|
R. Chitnis, G. Cormode, H. Esfandiari, M. Hajiaghayi, and M. Monemizadeh.
New streaming algorithms for parameterized maximal matching &
beyond.
In Symposium on Parallelism in Algorithms, pages 56-58, 2015.
|
|
[50]
|
J. Zhang, G. Cormode, M. Procopiuc, D. Srivastava, and X. Xiao.
Private release of graph statistics using ladder functions.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2015.
|
|
[51]
|
A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venkatasubramanian.
Verifiable stream computation and Arthur-Merlin communication.
In Computational Complexity Conference, 2015.
|
|
[52]
|
Z. Jorgensen, T. Yu, and G. Cormode.
Conservative or liberal? personalized differential privacy.
In International Conference on Data Engineering (ICDE), 2015.
|
|
[53]
|
R. Chitnis, G. Cormode, M. Hajiaghayi, and M. Monemizadeh.
Parameterized streaming: Maximal matching and vertex cover.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2015.
|
|
[54]
|
G. Cormode, M. Procopiuc, D. Srivastava, X. Xiao, and J. Zhang.
Privbayes: Private data release via bayesian networks.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2014.
|
|
[55]
|
Q. Ma, S. Muthukrishnan, B. Thompson, and G. Cormode.
Modeling collaboration in academia: A game theoretic approach.
In WWW Workshop on Big Scholarly Data, 2014.
|
|
[56]
|
G. Cormode, S. Muthukrishnan, and J. Yan.
People like us: Mining scholarly data for comparable researchers.
In WWW Workshop on Big Scholarly Data, 2014.
|
|
[57]
|
A. Chakrabarti, G. Cormode, N. Goyal, and J. Thaler.
Annotations for sparse data streams.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2014.
|
|
[58]
|
G. Cormode, S. Muthukrishnan, and J. Yan.
First author advantage: Citation labeling in research.
In Proceedings of the Computational Scientometrics: Theory and
Applications Workshop at CIKM, 2013.
|
|
[59]
|
G. Cormode, X. Gong, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu.
UMicS: From anonymized data to usable microdata.
In ACM Conference on Information and Knowledge Management
(CIKM), 2013.
|
|
[60]
|
S. Papadopoulos, G. Cormode, A. Deligiannakis, and M. Garofalakis.
Lightweight authentication of linear algebraic queries on data
streams.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2013.
|
|
[61]
|
L. Wang, G. Luo, K. Yi, and G. Cormode.
Quantiles over data streams: An experimental study.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2013.
|
|
[62]
|
G. Cormode, C. M. Procopiuc, E. Shen, D. Srivastava, and T. Yu.
Empirical privacy and empirical utility of anonymized data.
In Privacy-Preserving Data Publication and Analysis (PrivDB),
2013.
|
|
[63]
|
G. Cormode, K. Mirylenka, T. Palpanas, and D. Srivastava.
Finding interesting correlations with conditional heavy hitters.
In International Conference on Data Engineering (ICDE), 2013.
|
|
[64]
|
G. Cormode, C. M. Procopiuc, D. Srivastava, and G. Yaroslavtsev.
Accurate and efficient private release of datacubes and contingency
tables.
In International Conference on Data Engineering (ICDE), 2013.
|
|
[65]
|
G. Cormode and D. Firmani.
On unifying the space of l0-sampling algorithms.
In SIAM Meeting on Algorithm Engineering and Experiments,
2013.
|
|
[66]
|
G. Cormode, J. Thaler, and K. Yi.
Verifying computations with streaming interactive proofs.
In International Conference on Very Large Data Bases (VLDB),
Sept. 2012.
|
|
[67]
|
A. Goyal, H. Daumé III, and G. Cormode.
Sketch algorithms for estimating point queries in NLP.
In EMNLP-CoNLL, pages 1093-1103, 2012.
|
|
[68]
|
P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi.
Mergeable summaries.
In ACM Principles of Database Systems (PODS), 2012.
|
|
[69]
|
E. Cohen, G. Cormode, and N. Duffield.
Don't let the negatives bring you down: Sampling from streams of
signed updates.
In ACM Conference on Measurement and Modeling of Computer
Systems (SIGMETRICS), 2012.
|
|
[70]
|
G. Cormode and K. Yi.
Tracking distributed aggregates over time-based sliding windows.
In Scientific and Statistical Database Management (SSDBM),
2012.
|
|
[71]
|
G. Cormode, S. Muthukrishnan, and J. Yan.
Scienceography: the study of how science is written.
In Proceedings of the International Conference on Fun with
Algorithms (FUN), 2012.
|
|
[72]
|
G. Cormode, M. Procopiuc, D. Srivastava, and T. Tran.
Differentially private publication of sparse data.
In International Conference on Database Theory (ICDT), 2012.
|
|
[73]
|
G. Cormode, M. Procopiuc, E. Shen, D. Srivastava, and T. Yu.
Differentially private spatial decompositions.
In International Conference on Data Engineering (ICDE), 2012.
|
|
[74]
|
M. Lu, S. Bangalore, G. Cormode, M. Hadjieleftheriou, and D. Srivastava.
A dataset search engine for the research document corpus.
In International Conference on Data Engineering (ICDE), 2012.
|
|
[75]
|
G. Cormode, E. Shen, D. Srivastava, and T. Yu.
Aggregate query answering on possibilistic data with cardinality
constraints.
In International Conference on Data Engineering (ICDE), 2012.
|
|
[76]
|
G. Cormode, M. Mitzenmacher, and J. Thaler.
Practical verified computation with streaming interactive proofs.
In Innovations in Theoretical Computer Science (ITCS), 2012.
|
|
[77]
|
P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi.
Mergeable coresets.
In Third Workshop on Massive Data Algorithmics (MASSIVE), 2011.
|
|
[78]
|
G. Cormode.
Personal privacy vs population privacy: Learning to attack
anonymization.
In ACM SIGKDD Conference, 2011.
|
|
[79]
|
E. Cohen, G. Cormode, and N. Duffield.
Structure-aware sampling: Flexible and accurate summarization.
In International Conference on Very Large Data Bases (VLDB),
2011.
|
|
[80]
|
G. Cormode and K. Yi.
Tracking distributed aggregates over time-based sliding windows
(brief announcement).
In ACM Principles of Distributed Computing (PODC), 2011.
|
|
[81]
|
E. Cohen, G. Cormode, and N. Duffield.
Structure-aware sampling on data streams.
In ACM Conference on Measurement and Modeling of Computer
Systems (SIGMETRICS), 2011.
|
|
[82]
|
G. Cormode, H. Karloff, and T. Wirth.
Set cover algorithms for very large datasets.
In ACM Conference on Information and Knowledge Management
(CIKM), 2010.
|
|
[83]
|
G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang.
Optimal sampling from distributed streams.
In ACM Principles of Database Systems (PODS), 2010.
|
|
[84]
|
S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava.
Privacy in dynamic social networks.
In World Wide Web Conference (WWW), 2010.
|
|
[85]
|
G. Cormode, M. Mitzenmacher, and J. Thaler.
Streaming graph computations with a helpful advisor.
In European Symposium on Algorithms, 2010.
|
|
[86]
|
A. Chakrabarti, G. Cormode, R. Kondapally, and A. McGregor.
Information cost tradeoffs for augmented index and streaming language
recognition.
In IEEE Foundations of Computer Science (FOCS), 2010.
|
|
[87]
|
G. Cormode, N. Li, T. Li, and D. Srivastava.
Minimizing minimality and maximizing utility: Analyzing method-based
attacks on anonymized data.
In International Conference on Very Large Data Bases (VLDB),
2010.
|
|
[88]
|
S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava.
Prediction promotes privacy in dynamic social networks.
In Workshop on Online Social Networks (WOSN), 2010.
|
|
[89]
|
S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava.
Class-based graph anonymization for social network data.
In International Conference on Very Large Data Bases (VLDB),
2009.
|
|
[90]
|
G. Cormode, A. Deligiannakis, M. Garofalakis, and A. McGregor.
Probabilistic histograms for probabilistic data.
In International Conference on Very Large Data Bases (VLDB),
2009.
|
|
[91]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
Annotations in data streams.
In International Colloquium on Automata, Languages and
Programming (ICALP), 2009.
|
|
[92]
|
G. Cormode, L. Golab, F. Korn, A. McGregor, D. Srivastava, and X. Zhang.
Estimating the confidence of conditional functional dependencies.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2009.
|
|
[93]
|
R. Berinde, G. Cormode, P. Indyk, and M. Strauss.
Space-optimal heavy hitters with strong error bounds.
In ACM Principles of Database Systems (PODS), 2009.
|
|
[94]
|
G. Cormode, S. Tirthapura, and B. Xu.
Time-decayed correlated aggregates over data streams.
In SIAM Conference on Data Mining (SDM), 2009.
|
|
[95]
|
G. Cormode and M. Garofalakis.
Histograms and wavelets on probabilistic data.
In International Conference on Data Engineering (ICDE), 2009.
Best paper award.
|
|
[96]
|
G. Cormode, F. Li, and K. Yi.
Semantics of ranking queries for probabilistic data and expected
ranks.
In International Conference on Data Engineering (ICDE), 2009.
|
|
[97]
|
G. Cormode, V. Shkapenyuk, D. Srivastava, and B. Xu.
Forward decay: A practical time decay model for streaming systems.
In International Conference on Data Engineering (ICDE), 2009.
|
|
[98]
|
G. Cormode and M. Hadjieleftheriou.
Finding frequent items in data streams.
In International Conference on Very Large Data Bases (VLDB),
2008.
Best paper award.
|
|
[99]
|
G. Cormode, D. Srivastava, T. Yu, and Q. Zhang.
Anonymizing bipartite graph data using safe groupings.
In International Conference on Very Large Data Bases (VLDB),
2008.
|
|
[100]
|
G. Cormode, F. Korn, and S. Tirthapura.
Time-decaying aggregates in out-of-order streams.
In ACM Principles of Database Systems (PODS), 2008.
|
|
[101]
|
G. Cormode and A. McGregor.
Approximation algorithms for clustering uncertain data.
In ACM Principles of Database Systems (PODS), 2008.
|
|
[102]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Summarizing two-dimensional data with skyline-based statistical
descriptors.
In Scientific and Statistical Database Management (SSDBM),
2008.
|
|
[103]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
Robust lower bounds for communication and stream computation.
In ACM Symposium on Theory of Computing (STOC), 2008.
|
|
[104]
|
G. Cormode, F. Korn, S. Muthukrishnan, and Y. Wu.
On signatures for communication graphs.
In International Conference on Data Engineering (ICDE), 2008.
|
|
[105]
|
G. Cormode, F. Korn, and S. Tirthapura.
Exponentially decayed aggregates on data streams.
In International Conference on Data Engineering (ICDE), 2008.
|
|
[106]
|
G. Cormode, S. Muthukrishnan, and K. Yi.
Algorithms for distributed, functional monitoring.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2008.
|
|
[107]
|
S. Bhagat, G. Cormode, and I. Rozenbaum.
Applying link-based classification to label blogs.
In Joint WEBKDD and SNA-KDD Workshop, 2007.
|
|
[108]
|
S. Ganguly and G. Cormode.
On estimating frequency moments of data streams.
In Proceedings of RANDOM, 2007.
|
|
[109]
|
G. Cormode, S. Tirthapura, and B. Xu.
Time-decaying sketches for sensor data aggregation.
In ACM Principles of Distributed Computing (PODC), 2007.
|
|
[110]
|
G. Cormode and M. Garofalakis.
Sketching probabilistic data streams.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2007.
|
|
[111]
|
S. Bhagat, G. Cormode, S. Muthukrishnan, I. Rozenbaum, and H. Xue.
No blog is an island analyzing connections across information
networks.
In International Conference on Weblogs and Social Media, 2007.
|
|
[112]
|
G. Cormode, S. Muthukrishnan, and W. Zhuang.
Conquering the divide: Continuous clustering of distributed data
streams.
In International Conference on Data Engineering (ICDE), 2007.
|
|
[113]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
A near-optimal algorithm for computing the entropy of a stream.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2007.
|
|
[114]
|
G. Cormode and S. Muthukrishnan.
Combinatorial algorithms for compressed sensing.
In SIROCCO, 2006.
|
|
[115]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Space- and time-efficient deterministic algorithms for biased
quantiles over data streams.
In ACM Principles of Database Systems (PODS), 2006.
|
|
[116]
|
G. Cormode, R. Keralapura, and J. Ramimirtham.
Communication-efficient distributed monitoring of thresholded counts.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2006.
|
|
[117]
|
G. Cormode, M. Garofalakis, and D. Sacharidis.
Fast approximate wavelet tracking on streams.
In Extending Database Technology, pages 4-22, 2006.
|
|
[118]
|
G. Cormode, S. Muthukrishnan, and W. Zhuang.
What's different: Distributed, continuous monitoring of
duplicate-resilient aggregates on data streams.
In International Conference on Data Engineering (ICDE), pages
20-31, 2006.
|
|
[119]
|
G. Cormode, S. Muthukrishnan, and I. Rozenbaum.
Summarizing and mining inverse distributions on data streams via
dynamic inverse sampling.
In International Conference on Very Large Data Bases (VLDB),
pages 25-36, 2005.
|
|
[120]
|
G. Cormode and M. Garofalakis.
Sketching streams through the net: Distributed approximate query
tracking.
In International Conference on Very Large Data Bases (VLDB),
pages 13-24, 2005.
|
|
[121]
|
G. Cormode and S. Muthukrishnan.
Space efficient mining of multigraph streams.
In ACM Principles of Database Systems (PODS), pages
271-282, 2005.
|
|
[122]
|
G. Cormode, M. Garofalakis, S. Muthukrishnan, and R. Rastogi.
Holistic aggregates in a networked world: Distributed tracking of
approximate quantiles.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), pages 25-36, 2005.
|
|
[123]
|
G. Cormode and S. Muthukrishnan.
Summarizing and mining skewed data streams.
In SIAM Conference on Data Mining (SDM), 2005.
|
|
[124]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Effective computation of biased quantiles over data streams.
In International Conference on Data Engineering (ICDE), pages
20-31, 2005.
|
|
[125]
|
G. Cormode and S. Muthukrishnan.
Substring compression problems.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
pages 321-330, 2005.
|
|
[126]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Diamond in the rough: Finding hierarchical heavy hitters in
multi-dimensional data.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), pages 155-166, 2004.
|
|
[127]
|
G. Cormode, F. Korn, S. Muthukrishnan, T. Johnson, O. Spatscheck, and
D. Srivastava.
Holistic UDAFs at streaming speeds.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), pages 35-46, 2004.
|
|
[128]
|
G. Cormode.
The hardness of the lemmings game, or Oh no, more NP-completeness
proofs.
In Proceedings of Third International Conference on Fun with
Algorithms, pages 65-76, 2004.
|
|
[129]
|
G. Cormode, A. Czumaj, and S. Muthukrishnan.
How to increase the acceptance ratios of top conferences.
In Proceedings of Third International Conference on Fun with
Algorithms, pages 262-273, 2004.
|
|
[130]
|
G. Cormode and S. Muthukrishnan.
What's new: Finding significant differences in network data streams.
In Proceedings of IEEE Infocom, pages 1534-1545, 2004.
|
|
[131]
|
G. Cormode and S. Muthukrishnan.
An improved data stream summary: The count-min sketch and its
applications.
In Proceedings of Latin American Theoretical Informatics
(LATIN), pages 29-38, 2004.
|
|
[132]
|
G. Cormode.
Stable distributions for stream computations: it's as easy as 0,1,2.
In Workshop on Management and Processing of Massive Data Streams
at FCRC, 2003.
|
|
[133]
|
G. Cormode and S. Muthukrishnan.
What's hot and what's not: Tracking most frequent items dynamically.
In ACM Principles of Database Systems (PODS), pages
296-306, 2003.
|
|
[134]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Finding hierarchical heavy hitters in data streams.
In International Conference on Very Large Data Bases (VLDB),
pages 464-475, 2003.
|
|
[135]
|
G. Cormode and S. Muthukrishnan.
Estimating dominance norms of multiple data streams.
In European Symposium on Algorithms, volume 2838 of LNCS,
2003.
|
|
[136]
|
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan.
Comparing data streams using Hamming norms.
In International Conference on Very Large Data Bases (VLDB),
pages 335-345, 2002.
|
|
[137]
|
G. Cormode, P. Indyk, N. Koudas, and S. Muthukrishnan.
Fast mining of tabular data via approximate distance computations.
In International Conference on Data Engineering (ICDE), pages
605-616, 2002.
|
|
[138]
|
G. Cormode and S. Muthukrishnan.
The string edit distance matching problem with moves.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
pages 667-676, 2002.
|
|
[139]
|
G. Cormode, S. Muthukrishnan, and S. C. Sahinalp.
Permutation editing and matching via embeddings.
In International Colloquium on Automata, Languages and
Programming (ICALP), volume 2076, pages 481-492, 2001.
|
|
[140]
|
G. Cormode, M. Paterson, S. C. Sahinalp, and U. Vishkin.
Communication complexity of document exchange.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
pages 197-206, 2000.
|
|
[141]
|
G. Ozsoyoglu, N. H. Balkir, G. Cormode, and Z. M. Ozsoyoglu.
Electronic books in digital libraries.
In Proceedings of IEEE Advances in Digital Libraries (ADL),
pages 5-14, 2000.
|
|
[1]
|
A. Bharadwaj and G. Cormode.
Federated computation: a survey of concepts and challenges.
Distributed and Parallel Databases, 42(3):299-335, 2024.
|
|
[2]
|
K. Prasad, S. Ghosh, G. Cormode, I. Mironov, A. Yousefpour, and P. Stock.
Reconciling security and communication efficiency in federated
learning.
46(1), 2023.
|
|
[3]
|
G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and Veselý.
Relative error streaming quantiles.
Journal of the ACM (JACM), 70(5):1-48, 2023.
|
|
[4]
|
G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and Veselý.
Relative error streaming quantiles.
SIGMOD Record, 51(1):66-79, Mar. 2022.
|
|
[5]
|
G. Cormode.
Current trends in data summaries.
SIGMOD Record, 50(4):6–15, Jan. 2022.
|
|
[6]
|
G. Cormode, C. Maple, and M. Scott.
Aggregation and transformation of vector-valued messages in the
shuffle model of differential privacy.
IEEE Trans. Inf. Forensics Secur., 17:612-627, 2022.
|
|
[7]
|
G. Cormode, T. Kulkarni, and D. Srivastava.
Constrained private mechanisms for count data.
IEEE Transactions on Knowledge and Data Engineering,
33(2):415-430, Feb. 2021.
|
|
[8]
|
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. A.
Bonawitz, Z. Charles, G. Cormode, R. Cummings, R. G. L. D'Oliveira,
H. Eichner, S. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett,
A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He,
L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi,
M. Khodak, J. Konečný, A. Korolova, F. Koushanfar, S. Koyejo,
T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür,
R. Pagh, H. Qi, D. Ramage, R. Raskar, M. Raykova, D. Song, W. Song, S. U.
Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang,
L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, and S. Zhao.
Advances and open problems in federated learning.
Foundations and Trends in Machine Learning, 14(1-2):1-210,
2021.
|
|
[9]
|
K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth.
Correlation clustering in data streams.
Algorithmica, 83(7):1980-2017, 2021.
|
|
[10]
|
G. Cormode and P. Veselý.
Streaming algorithms for bin packing and vector scheduling.
Theory of Computing Systems, 65(6):916-942, 2021.
|
|
[11]
|
A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venktatasubramanian.
Verifiable stream computation and Arthur-Merlin communication.
SIAM Journal on Computing (SICOMP), 2019.
|
|
[12]
|
G. Cormode and H. Jowhari.
lp samplers and their applications: A survey.
ACM Computing Surveys, 2018.
|
|
[13]
|
G. Cormode, A. Dasgupta, A. Goyal, and C. H. Lee.
An evaluation of multi-probe locality sensitive hashing for computing
similarities over web-scale query logs.
PLOS ONE, 13(1):e0191175, 2018.
|
|
[14]
|
J. Zhang, G. Cormode, M. Procopiuc, D. Srivastava, and X. Xiao.
Privbayes: Private data release via bayesian networks.
ACM Transactions on Database Systems, 2017.
|
|
[15]
|
G. Cormode.
Data sketching.
Communications of the ACM (CACM), 60(9):48-55, 2017.
|
|
[16]
|
G. Cormode and H. Jowhari.
A second look at counting triangles in graph streams (revised).
Theoretical Computer Science, 683:22-30, 2017.
|
|
[17]
|
E. Cohen, G. Cormode, N. Duffield, and C. Lund.
On the tradeoff between stability and fit.
ACM Transactions on Algorithms, 13(1), 2016.
|
|
[18]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
Robust lower bounds for communication and stream computation.
Theory of Computing, 12(10):1-35, 2016.
|
|
[19]
|
G. Luo, L. Wang, K. Yi, and G. Cormode.
Quantiles over data streams: experimental comparisons, new analyses,
and further improvements.
The VLDB Journal, 25(4):449-472, 2016.
|
|
[20]
|
K. Mirylenka, G. Cormode, T. Palpanas, and D. Srivastava.
Conditional heavy hitters: detecting interesting correlations in data
streams.
The VLDB Journal, 24(3):395-414, 2015.
|
|
[21]
|
S. Papadopoulos, G. Cormode, A. Deligiannakis, and M. N. Garofalakis.
Lightweight query authentication on streams.
ACM Transactions on Database Systems, 39(4):30:1-30:45,
2015.
|
|
[22]
|
A. Chakrabarti, G. Cormode, A. McGregor, and J. Thaler.
Annotations in data streams.
ACM Transactions on Algorithms, 11(1), 2014.
|
|
[23]
|
G. Cormode and D. Firmani.
A unifying framework for l0-sampling algorithms.
Distributed and Parallel Databases, 32(3):315-335, 2014.
Special issue on Data Summarization on Big Data.
|
|
[24]
|
G. Cormode.
What does an associate editor actually do?
SIGMOD Record, 42(2):52-58, June 2013.
|
|
[25]
|
G. Cormode.
The continuous distributed monitoring model.
SIGMOD Record, 42(1), Mar. 2013.
|
|
[26]
|
P. K. Agarwal, G. Cormode, Z. Huang, J. M. Phillips, Z. Wei, and K. Yi.
Mergeable summaries.
ACM Transactions on Database Systems, 38(4):26, 2013.
|
|
[27]
|
A. Chakrabarti, G. Cormode, R. Kondapally, and A. McGregor.
Information cost tradeoffs for augmented index and streaming language
recognition.
SIAM Journal on Computing (SICOMP), 42(1):61-83, 2013.
|
|
[28]
|
G. Cormode, Q. Ma, S. Muthukrishnan, and B. Thompson.
Socializing the h-index.
Journal of Informetrics, 7(3):718 - 721, 2013.
|
|
[29]
|
G. Cormode, M. Mitzenmacher, and J. Thaler.
Streaming graph computations with a helpful advisor.
Algorithmica, 65(2):409-442, 2013.
|
|
[30]
|
G. Cormode, S. Muthukrishnan, and J. Yan.
Studying the source code of scientific research.
SIGKDD Explorations, 14(2):59-62, Dec. 2012.
|
|
[31]
|
G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang.
Continuous sampling from distributed streams.
Journal of the ACM (JACM), 59(2), Apr. 2012.
|
|
[32]
|
G. Cormode and S. Muthukrishnan.
Approximating data with the count-min data structure.
IEEE Software, 2012.
|
|
[33]
|
G. Cormode, S. Muthukrishnan, and K. Yi.
Algorithms for distributed functional monitoring.
ACM Transactions on Algorithms, 7(2):1-21, 2011.
|
|
[34]
|
G. Cormode, J. Jestes, F. Li, and K. Yi.
Semantics of ranking queries for probabilistic data.
IEEE Transactions on Knowledge and Data Engineering,
23(12):1903-1917, 2011.
|
|
[35]
|
G. Cormode, B. Krishnamurthy, and W. Willinger.
A manifesto for modeling and measurement in social media.
First Monday, 15(9), Sept. 2010.
|
|
[36]
|
G. Cormode and M. Garofalakis.
Histograms and wavelets on probabilistic data.
IEEE Transactions on Knowledge and Data Engineering,
22(8):1142-1157, Aug. 2010.
|
|
[37]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
A near-optimal algorithm for computing the entropy of a stream.
ACM Transactions on Algorithms, 6(3), 2010.
|
|
[38]
|
R. Berinde, G. Cormode, P. Indyk, and M. Strauss.
Space-optimal heavy hitters with strong error bounds.
ACM Transactions on Database Systems, 35(4), 2010.
|
|
[39]
|
G. Cormode, D. Srivastava, T. Yu, and Q. Zhang.
Anonymizing bipartite graph data using safe groupings.
The VLDB Journal, 19(1):115-139, 2010.
|
|
[40]
|
G. Cormode and M. Hadjieleftheriou.
Methods for finding frequent items in data streams.
The VLDB Journal, 19(1):3-20, 2010.
|
|
[41]
|
G. Cormode, S. Tirthapura, and B. Xu.
Time-decaying sketches for robust aggregation of sensor data.
SIAM Journal on Computing (SICOMP), 39(4):1309-1339, 2009.
|
|
[42]
|
G. Cormode, S. Tirthapura, and B. Xu.
Time-decayed correlated aggregates over data streams.
Statistical Analysis and Data Mining, 2(5-6):294-310, 2009.
|
|
[43]
|
K. Yi, F. Li, G. Cormode, M. Hadjieleftheriou, G. Kollios, and D. Srivastava.
Small synopses for group-by query verification on outsourced data
streams.
ACM Transactions on Database Systems, 34(3), 2009.
|
|
[44]
|
G. Cormode and M. Hadjieleftheriou.
Finding the frequent items in streams of data.
Communications of the ACM (CACM), 52(10):97-105, 2009.
|
|
[45]
|
G. Cormode.
How not to review a paper: The tools and techniques of the
adversarial reviewer.
SIGMOD Record, 37(4):100-104, Dec. 2008.
|
|
[46]
|
G. Cormode and B. Krishnamurthy.
Key differences between web 1.0 and web 2.0.
First Monday, 13(6), June 2008.
|
|
[47]
|
G. Cormode and M. Garofalakis.
Approximate continuous querying over distributed streams.
ACM Transactions on Database Systems, 33(2), June 2008.
|
|
[48]
|
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava.
Finding hierarchical heavy hitters in streaming data.
ACM Transactions on Knowledge Discovery from Data (TKDD),
1(4), Jan. 2008.
|
|
[49]
|
G. Cormode and S. Muthukrishnan.
The string edit distance matching problem with moves.
ACM Transactions on Algorithms, 3(1), 2007.
|
|
[50]
|
G. Cormode and S. Muthukrishnan.
What's new: Finding significant differences in network data streams.
Transactions on Networking, 13(6):1219-1232, December 2005.
|
|
[51]
|
G. Cormode and S. Muthukrishnan.
An improved data stream summary: The count-min sketch and its
applications.
Journal of Algorithms, 55(1):58-75, April 2005.
|
|
[52]
|
G. Cormode and S. Muthukrishnan.
What's hot and what's not: Tracking most frequent items dynamically.
ACM Transactions on Database Systems, 30(1):249-278, March
2005.
|
|
[53]
|
G. Cormode.
Representations of the research student in popular culture.
Annals of Improbable Research, 10(1):26-27, 2004.
|
|
[54]
|
G. Ozsoyoglu, N. H. Balkir, G. Cormode, and Z. M. Ozsoyoglu.
Electronic books in digital libraries.
IEEE Transactions on Knowledge and Data Engineering,
16(3):317-331, 2004.
|
|
[55]
|
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan.
Comparing data streams using Hamming norms.
IEEE Transactions on Knowledge and Data Engineering,
15(3):529-541, 2003.
|
|
[1]
|
G. Cormode.
Technical perspective on 'better differentially private approximate
histograms and heavy hitters using the misra-gries sketch.
In SIGMOD Record, volume 53(1), page 6. ACM, Mar. 2024.
|
|
[2]
|
G. Cormode.
Technical perspective on `R2T: Instance-optimal Truncation for
Differentially Private Query Evaluation with Foreign Keys'.
In SIGMOD Record, volume 52(1), page 114. ACM, Mar. 2023.
|
|
[3]
|
G. Cormode.
Gems of pods: Applications of sketching and pathways to impact.
In ACM Principles of Database Systems (PODS). ACM, 2023.
|
|
[4]
|
G. Cormode.
Technical perspective: A framework for adversarially robust streaming
algorithms.
In SIGMOD Record, volume 50(1). ACM, Mar. 2021.
Introduction to research highlights paper by Ben-Eliezer, Jayaram,
Woodruff, and Yogev.
|
|
[5]
|
G. Cormode.
The true cost of popularity.
In Communications of the ACM (CACM). ACM, July 2019.
Introduction to research highlights paper by Larsen, Nelson, Nguyen
and Thorup.
|
|
[6]
|
G. Cormode.
Technical perspective: εktelo: A framework for
defining differentially-private computations.
In SIGMOD Record, volume 48(1). ACM, Mar. 2019.
Introduction to research highlights paper by Zhang, McKenna,
Kotsogiannis, Bissias, Hay, Machanavajjhala and Gerome Miklau.
|
|
[7]
|
G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan.
The sparse awakens: Streaming algorithms for matching size estimation
in sparse graphs.
Technical report, ArXiV, 2016.
|
|
[8]
|
G. Cormode and M. Garofalakis.
Join sizes, frequency moments, and applications.
In M. Garofalakis, J. Gehrke, and R. Rastogi, editors, Data
Stream Management: Processing High-Speed Data Streams. Springer, 2016.
|
|
[9]
|
G. Cormode and P. Indyk.
Stable distributions in streaming computations.
In M. Garofalakis, J. Gehrke, and R. Rastogi, editors, Data
Stream Management: Processing High-Speed Data Streams. Springer, 2016.
|
|
[10]
|
G. Cormode.
Encyclopedia entry on 'count-min sketch'.
In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer,
2015.
|
|
[11]
|
G. Cormode.
Encyclopedia entry on 'ams sketch'.
In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer,
2015.
|
|
[12]
|
G. Cormode.
Encyclopedia entry on 'misra-gries summary'.
In M.-Y. Kao, editor, Encyclopedia of Algorithms. Springer,
2015.
|
|
[13]
|
G. Cormode.
Continuous distributed monitoring: A short survey.
In Paper accompanying invited talk at Algorithms and Models for
Distributed Event Processing (AlMoDEP). ACM, Sept. 2011.
|
|
[14]
|
G. Cormode.
Sketch techniques for massive data.
In G. Cormode, M. Garofalakis, P. Haas, and C. Jermaine, editors,
Synposes for Massive Data: Samples, Histograms, Wavelets and Sketches,
Foundations and Trends in Databases. NOW publishers, 2011.
|
|
[15]
|
S. Bhagat, G. Cormode, and S. Muthukrishnan.
Node classification in social networks.
In C. C. Aggarwal, editor, Social Network Data Analytics.
Springer, 2011.
|
|
[16]
|
G. Cormode, J. Thaler, and K. Yi.
Verifying computations with streaming interactive proofs.
Technical Report TR10-159, Electronic Colloquium on Computational
Complexity (ECCC), 2010.
|
|
[17]
|
G. Cormode.
Individual privacy vs population privacy: Learning to attack
anonymization.
Technical Report arXiv:1011.2511, arXiv, 2010.
|
|
[18]
|
G. Cormode.
Encyclopedia entry on 'Count-Min Sketch'.
In L. Liu and M. T. Ozsu, editors, Encyclopedia of Database
Systems, pages 511-516. Springer, 2009.
|
|
[19]
|
G. Cormode and M. Garofalakis.
Histograms and wavelets on probabilistic data.
Technical Report arXiv:0806.1071, arXiv, 2008.
|
|
[20]
|
G. Cormode, F. Korn, and S. Tirthapura.
Time decaying aggregates in out-of-order streams.
Technical Report 2007-10, Center for Discrete Mathematics and
Computer Science (DIMACS), 2007.
|
|
[21]
|
G. Cormode.
Computational fundamentals of analyzing and mining data streams.
In Workshop on Data Stream Analysis. 2007.
|
|
[22]
|
G. Cormode and S. Muthukrishnan.
Combinatorial algorithms for compressed sensing.
In Proceedings of Conference on Information Sciences and Systems
(CISS). IEEE, 2006.
Invited submission.
|
|
[23]
|
G. Cormode.
Some key concepts in data mining - clustering.
In Discrete Methods in Epidemiology, volume 70 of DIMACS,
pages 2-9. AMS, 2006.
|
|
[24]
|
G. Cormode and M. Garofalakis.
Efficient strategies for continuous distributed tracking tasks.
In IEEE Data Engineering Bulletin, pages 33-39. IEEE, March
2005.
|
|
[25]
|
G. Cormode and S. Muthukrishnan.
Combinatorial algorithms for compressed sensing.
Technical Report 2005-40, Center for Discrete Mathematics and
Computer Science (DIMACS), 2005.
|
|
[26]
|
G. Cormode and S. Muthukrishnan.
Towards an algorithmic theory of compressed sensing.
Technical Report 2005-25, Center for Discrete Mathematics and
Computer Science (DIMACS), 2005.
|
|
[27]
|
G. Cormode, S. Muthukrishnan, and I. Rozenbaum.
Summarizing and mining inverse distributions on data streams via
dynamic inverse sampling.
Technical Report 2005-11, Center for Discrete Mathematics and
Computer Science (DIMACS), 2005.
|
|
[28]
|
G. Cormode.
The hardness of the lemmings game, or, “Oh no, more
NP-Completeness proofs”.
Technical Report 2004-11, Center for Discrete Mathematics and
Computer Science (DIMACS), 2004.
|
|
[29]
|
J. Abello and G. Cormode.
Report on DIMACS working group meeting: Data mining and
epidemiology, March 18-19, 2004.
Technical Report 2004-37, Center for Discrete Mathematics and
Computer Science (DIMACS), 2004.
|
|
[30]
|
G. Cormode, A. Czumaj, and S. Muthukrishnan.
How to increase the acceptance ratios of top conferences.
Technical Report 2004-12, Center for Discrete Mathematics and
Computer Science (DIMACS), 2004.
|
|
[31]
|
G. Cormode and S. Muthukrishnan.
Radial histograms for spatial streams.
Technical Report 2003-11, Center for Discrete Mathematics and
Computer Science (DIMACS), 2003.
|
|
[32]
|
G. Cormode and S. Muthukrishnan.
Estimating dominance norms of multiple data streams.
Technical Report 2002-35, Center for Discrete Mathematics and
Computer Science (DIMACS), 2002.
|
|
[33]
|
G. Cormode and S. Muthukrishnan.
The string edit distance matching problem with moves.
Technical Report 2001-26, Center for Discrete Mathematics and
Computer Science (DIMACS), 2001.
|
|
[34]
|
G. Cormode.
Topic dependencies for electronic books.
(unpublished manuscript), 1999.
|
|
[35]
|
G. Cormode.
Springs and sound layouts.
(unpublished manuscript), 1998.
|
|
[1]
|
Federated computation beyond learning, 2024.
Talk at FedKDD workshop.
|
|
[2]
|
Private data analysis over large populations, 2024.
Talk at Birmingham and Cambridge.
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|
[3]
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An introduction to federated computation, June 2022.
Tutorial presented at SIGMOD and the Web Conference.
|
|
[4]
|
Mergeable summaries, 2022.
PODS 2022 Test-of-time award presentation.
|
|
[5]
|
Frequency estimation in local and multiparty differential privacy, May 2021.
Invited talk at Distributed and Private Machine Learning Workshop.
|
|
[6]
|
New lower and upper bounds for quantile summary algorithms, Nov. 2020.
IGAFIT colloquium and Bar-Ilan University Colloquium.
|
|
[7]
|
Towards federated analytics with local differential privacy, Oct. 2020.
Talk at Facebook and Google.
|
|
[8]
|
Scaling up by scaling down, Feb. 2020.
Presentation at the Alan Turing Institute workshop on Data Science
and AI at Scale.
|
|
[9]
|
Distributed private data collection at scale, Jan. 2020.
Talks at Amazon Research and Samsung Research, Cambridge.
|
|
[10]
|
Local differential privacy: Solution or distraction?, June 2019.
Talk at Google Workshop on Federated Learning and Analytics.
|
|
[11]
|
Data science and privacy preservation, June 2019.
Tutorial at Trust in Data Science Summer School in Ghent.
|
|
[12]
|
Distributed private data collection at scale, 2019.
Talk at Edinburgh University, University of Washington.
|
|
[13]
|
Data summarization for machine learning, Jan. 2019.
Talk at Computer Science Research Week 2019, National University of
Singapore.
|
|
[14]
|
Data summarization and distributed computation, 2018.
Keynote talk at PODC 2018.
|
|
[15]
|
G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang.
Privacy at scale: Local differential privacy in practice, 2018.
Tutorial at SIGMOD and KDD.
|
|
[16]
|
Distributed private data collection at scale, Jan. 2018.
Talk at HiPEDs CDT (Imperial).
|
|
[17]
|
Locally private release of marginal statistics, Nov. 2017.
Talk at Google (Zurich) Algorithms and Optimization Day.
|
|
[18]
|
The confounding problem of private data release, Sept. 2017.
Talk at EPSRC Workshop on Future Research Directions in Demand
Management, Oxford University (CS).
|
|
[19]
|
Engineering streaming algorithms, June 2017.
Invited talk at Symposium on Experimental Algorithms.
|
|
[20]
|
Engineering privacy for small groups, Nov. 2016.
Talk at Isaac Newton Institute.
|
|
[21]
|
Matching and covering in streaming graphs, Sept. 2016.
Invited keynote talk at DISC 2016.
|
|
[22]
|
The confounding problem of private data release, Sept. 2016.
Invited talk at Heilbronn Conference; WMG; Liverpool University.
|
|
[23]
|
Sub-quadratic recovery of correlated pairs, June 2016.
Talk at Google Research, Facebook, Simons Institute, Manchester U.,
LSE.
|
|
[24]
|
Compact summaries over large datasets, May 2015.
Invited tutorial in PODS 2015 and BICOD 2015.
|
|
[25]
|
Trusting the cloud with practical interactive proofs, Apr. 2015.
Talk at Google NYC, Bristol, Oxford Algorithms Day, Durham.
|
|
[26]
|
The confounding problem of private data release, Mar. 2015.
Invited talk at EDBT/ICDT 2015.
|
|
[27]
|
Sampling for big data, Aug. 2014.
Tutorial at SIGKDD 2014 conference.
|
|
[28]
|
Sketches, streaming and big data, July 2014.
Summer school on Hashing at University of Copenhagen.
|
|
[29]
|
Differentially private mechanisms for data release, March 2014.
Talk at Hamilton Institute; Edinburgh University; Yahoo! Research,
New York.
|
|
[30]
|
Sketch data structures and concentration bounds / mergeable summaries, Sept.
2013.
Invited tutorial at Yandex conference.
|
|
[31]
|
Streaming, sketching and sufficient statistics, Sept. 2013.
Invited tutorial at Big Data Boot Camp, Simons Institute for
Theoretical Computer Science, Berkeley.
|
|
[32]
|
Summary data structures for massive data, July 2013.
Invited talk in Session on Data Streams and Compression,
Computability in Europe 2013.
|
|
[33]
|
Computing + statistics = data science, June 2013.
An introduction to data science for teenagers, IGGY DUX awards,
Experience Warwick.
|
|
[34]
|
Streaming verification of outsourced computation, May 2013.
Talk at Big Data Analytics Workshop, Microsoft Research Cambridge,
and University of Warwick.
|
|
[35]
|
Building blocks of privacy: Differentially private mechanisms, Apr. 2013.
Invited tutorial talk at Privacy Preserving Data Publication and
Analysis (PrivDB) workshop.
|
|
[36]
|
Privacy and big data: Challenges and promise, Mar. 2013.
Invited panel at NYU Abu Dhabi conference on Big Data Systems,
Applications, and Privacy.
|
|
[37]
|
Current industry trends in computer science research, Mar. 2013.
Invited Talk/Panel at NSF Research Experience for Undergraduates PI
Meeting.
|
|
[38]
|
Data-driven concerns in private data release, Sept. 2012.
Talk at Stevens Institute of Technology; AT&T Labs; UMass Amherst;
Rutgers University-Newark; Bell Labs; NYU-Abu Dhabi.
|
|
[39]
|
Sketches: Past, present and future, 2012.
Invited Panel on Sketching and Streaming at SAMSI Workshop, 2012.
|
|
[40]
|
Small summaries for Big Data, 2012.
Talk at Duke ARO workshop on Big Data at Large; MSR Cambridge;
Princeton.
|
|
[41]
|
Continuous distributed monitoring: A short survey, Sept. 2011.
Invited keynote at Algorithms and Models for Distributed Event
Processing (AlMoDEP).
|
|
[42]
|
Some sketchy results, May 2011.
Talk at DIMACS Workshop on Algorithms in the Field (8F).
|
|
[43]
|
Mergeable summaries, Apr. 2011.
Talk at Harvard University; DIMACS; Johns Hopkins; University of
Pennsylvania; AT&T Labs; Warwick University.
|
|
[44]
|
Data anonymization, Mar. 2011.
Guest lecture in 'Dealing with Massive Data' at Columbia University.
|
|
[45]
|
Distributed summaries, 2011.
Talk at DIMACS workshop on Parallelism: a 2020 vision.
|
|
[46]
|
G. Cormode and D. Srivastava.
Anonymized data: Generation, models, usage, Mar. 2010.
Tutorial at ICDE 2010.
|
|
[47]
|
Sipping from the firehose: Streaming interactive proofs for verifying
computations, February 2010.
Invited talk at Bristol Algorithms Days 2010; University of Maryland.
|
|
[48]
|
Progress in data anonymization: from k-anonymity to the minimality attack,
February 2010.
Talk in Bristol.
|
|
[49]
|
Anonymization and uncertainty in social network data, Oct. 2009.
Invited talk at DBIR Day 2009 at NYU Poly.
|
|
[50]
|
G. Cormode and D. Srivastava.
Anonymized data: Generation, models, usage, July 2009.
Tutorial at SIGMOD 2009.
|
|
[51]
|
Processing graph streams: Upper and lower bounds, June 2009.
Talk at Workshop on Algorithms and Models for Complex Networks,
Bristol UK.
|
|
[52]
|
Finding frequent items in data streams, March 2009.
Talk at DIMACS Working group on Streaming, Coding and Compressive
Sensing; AT&T Labs; UMass Amherst; Dartmouth College.
|
|
[53]
|
On 'selection and sorting with limited storage', Sept. 2008.
Talk at Mike66 Workshop celebrating Mike Paterson.
|
|
[54]
|
Algorithms for distributed functional monitoring, Aug. 2008.
Talk at Dagstuhl Seminar on Sublinear Algorithms.
|
|
[55]
|
Data stream algorithms, July 2008.
Tutorial at Bristol Summer School on Probabilistic Techniques in
Computer Science.
|
|
[56]
|
G. Cormode and M. Garofalakis.
Streaming in a connected world: Querying and tracking distributed
data streams, March 2008.
Tutorial at VLDB 2006, SIGMOD 2007, EDBT 2008.
|
|
[57]
|
Analyzing web 2.0, blogs and social networks, Dec. 2007.
Talk at AT&T Labs.
|
|
[58]
|
Computational fundamentals of analyzing and mining data streams, March 2007.
Tutorial at Workshop on Data Stream Analysis, Caserta, Italy.
|
|
[59]
|
Computing the entropy of a stream, December 2006.
AT&T Labs; Bell Labs; DyDAn Center.
|
|
[60]
|
A compact survey of compressed sensing, December 2006.
Workshop on Algorithms for Data Streams, IIT Kanpur, India.
|
|
[61]
|
Biased quantiles, June 2006.
Bertinoro.
|
|
[62]
|
Cluster and data stream analysis, March 2006.
Tutorial at DIMACS Workshop on Data Mining and Epidemiology.
|
|
[63]
|
Tracking inverse distributions of massive data streams, July 2005.
Network Sampling Workshop in Paris, Bell Labs Research Seminar.
|
|
[64]
|
Towards an algorithmic theory of compressed sensing, July 2005.
Schloss Dagstuhl.
|
|
[65]
|
Summarizing and mining skewed data streams, May 2005.
NJIT.
|
|
[66]
|
Algorithms for processing massive data at network line speed, March 2004.
Talk at U. Iowa; U. Minnesota; Dartmouth; Google; AT&T; CWRU; Poly.
|
|
[67]
|
How hard are computer games?, February 2004.
Talk at DIMACS.
|
|
[68]
|
What's hot, what's not, what's new and what's next, October 2003.
Bell Labs; DIMACS Mixer at AT&T Labs.
|
|
[69]
|
Zeroing in on the l0 metric, August 2003.
DIMACS Workshop on Discrete Metric Spaces and their Algorithmic
Applications at Princeton.
|
|
[70]
|
Tracking frequent items dynamically, 2003.
Institute of Advanced Studies; DIMACS; Stonybrook; U. Pennsylvania.
|
|
[71]
|
Algorithmic embeddings for comparing large text streams, June 2002.
CCR/DIMACS Workshop/Tutorial on Mining Massive Data Sets and Streams:
Mathematical Methods and Algorithms for Homeland Defense.
|
|
[72]
|
Embeddings of metrics on strings and permuations, March 2002.
Workshop on Discrete Metric Spaces and their Algorithmic Applications
in Haifa, Israel; BCTCS.
|
|
[73]
|
Short string signatures, September 2000.
DIMACS Workshop on Sublinear Algorithms in Princeton, NJ.
|