[1]
|
G. Cormode, S. Maddock, E. Ullah, and S. Gade.
Synthetic tabular data: Methods, attacks and defenses.
In ACM SIGKDD Conference, 2025.
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[2]
|
G. Cormode and D. Ting.
Federated data distribution shift estimation.
In Proceedings of the VLDB Endowment, volume 18, pages
2399-2412, 2025.
|
[3]
|
S. Maddock, S. Gade, G. Cormode, and W. Bullock.
Leveraging vertical public-private split for improved synthetic data
generation.
2025.
|
[4]
|
H. Srinivas, G. Cormode, M. Honarkhah, S. Lurye, J. Hehir, L. He, G. Hong,
A. Magdy, D. Huba, K. Wang, S. Guo, and S. Bhattacharya.
PAPAYA federated analytics stack: Engineering privacy, scalability
and practicality.
In USENIX Symposium on Networked Systems Design and
Implementation, NSDI, pages 883-898. USENIX Association, 2025.
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[5]
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A. Biswas, G. Cormode, Y. Kanza, D. Srivastava, and Z. Zhou.
Differentially private hierarchical heavy hitters.
In ACM Principles of Database Systems (PODS), 2025.
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[6]
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G. Cormode, S. Maddock, and C. Maple.
FLAIM: AIM-based synthetic data generation in the federated
setting.
In ACM SIGKDD Conference, 2024.
|
[7]
|
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.
|
[8]
|
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.
|
[9]
|
G. Cormode, M. Dall'Agnol, T. Gur, and C. Hickey.
Streaming zero-knowledge proofs.
In Computational Complexity Conference (CCC), 2024.
|
[10]
|
G. Cormode and I. L. Markov.
Federated calibration and evaluation of binary classifiers.
In Proceedings of the VLDB Endowment, volume 16(11), pages
3253-3265, 2023.
|
[11]
|
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.
|
[12]
|
J. Hehir, D. Ting, and G. Cormode.
Sketch-flip-merge: Mergeable sketches for private distinct counting.
In International Conference on Machine Learning, (ICML),
2023.
|
[13]
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A. Biswas and G. Cormode.
Interactive proofs for differentially private counting.
In ACM Conference on Computer and Communications Security,
2023.
|
[14]
|
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.
|
[15]
|
M. Shekelyan, G. Cormode, P. Triantafillou, Q. Ma, and A. M. Shanghooshabad.
Streaming weighted sampling over join queries.
In EDBT, pages 298-310, 2023.
|
[16]
|
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.
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[17]
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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.
|
[18]
|
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.
|
[19]
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A. Bharadwaj and G. Cormode.
Sample-and-threshold differential privacy: Histograms and
applications.
In AISTATS, 2022.
(full version).
|
[20]
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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, pages 2058-2070. VLDB Endowment, 2022.
|
[21]
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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.
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[22]
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A. Bharadwaj and G. Cormode.
Sample-and-threshold differential privacy: Histograms and
applications.
In Privacy in Machine Learning (NeurIPS workshop), 2021.
(workshop version).
|
[23]
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G. Cormode and I. Markov.
Bit-efficient numerical aggregation and stronger privacy for trust in
federated analytics.
In PPML Workshop, 2021.
|
[24]
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G. Cormode, C. Maple, and M. Scott.
Applying the shuffle model of differential privacy to vector
aggregation.
In BICOD, 2021.
|
[25]
|
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.
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[26]
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G. Cormode, S. Maddock, and C. Maple.
Frequency estimation under local differential privacy.
In International Conference on Very Large Data Bases (VLDB),
2021.
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[27]
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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.
|
[28]
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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.
|
[29]
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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.
|
[30]
|
G. Cormode, Z. Karnin, E. Liberty, J. Thaler, and P. Veselý.
Relative error streaming quantiles.
In ACM Principles of Database Systems (PODS), 2021.
|
[31]
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G. Cormode, C. Dickens, and D. Woodruff.
Subspace exploration: Bounds on projected frequency estimation.
In ACM Principles of Database Systems (PODS), 2021.
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[32]
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G. Cormode, M. Garofalakis, and M. Shekelyan.
Data-independent space partitionings for summaries.
In ACM Principles of Database Systems (PODS), 2021.
|
[33]
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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.
|
[34]
|
G. Cormode and C. Dickens.
Iterative hessian sketch in input sparsity time.
In Proceedings of Beyond First Order Methods in ML (NeurIPS
workshop), 2019.
|
[35]
|
G. Cormode and C. Hickey.
Efficient interactive proofs for linear algebra.
In Proceedings of International Symposium on Algorithms and
Computation (ISAAC), 2019.
|
[36]
|
G. Cormode and P. Veselý.
Streaming algorithms for bin packing and vector scheduling.
In Workshop on Approximation and Online Algorithms, 2019.
|
[37]
|
R. Chitnis and G. Cormode.
Towards a theory of parameterized streaming algorithms.
In International Symposium on Parameterized and Exact
Computation, 2019.
|
[38]
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G. Cormode, T. Kulkarni, and D. Srivastava.
Answering range queries under local differential privacy.
In International Conference on Very Large Data Bases (VLDB),
2019.
|
[39]
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G. Cormode, J. Dark, and C. Konrad.
Independent sets in vertex-arrival streams.
In International Colloquium on Automata, Languages and
Programming (ICALP), 2019.
|
[40]
|
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.
|
[41]
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G. Cormode, T. Kulkarni, and D. Srivastava.
Marginal release under local differential privacy.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2018.
|
[42]
|
G. Cormode and C. Hickey.
You can check others' work more quickly than doing it yourself.
In International Conference on Data Engineering (ICDE), 2018.
|
[43]
|
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.
|
[44]
|
Y. Zhang, S. Tirthapura, and G. Cormode.
Learning graphical models from a distributed stream.
In International Conference on Data Engineering (ICDE), 2018.
|
[45]
|
G. Cormode and J. Dark.
Fast sketch-based recovery of correlation outliers.
In International Conference on Database Theory, 2018.
|
[46]
|
G. Cormode and C. Hickey.
Cheap checking for cloud computing: Statistical analysis via
annotated data streams.
In AISTATS, 2018.
|
[47]
|
G. Cormode, T. Kulkarni, and D. Srivastava.
Constrained private mechanisms for count data.
In International Conference on Data Engineering (ICDE), 2018.
|
[48]
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G. Cormode, H. Jowhari, M. Monemizadeh, and S. Muthukrishnan.
Streaming algorithms for matching size estimation in sparse graphs.
In European Symposium on Algorithms, 2017.
|
[49]
|
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.
|
[50]
|
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.
|
[51]
|
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.
|
[52]
|
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.
|
[53]
|
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.
|
[54]
|
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.
|
[55]
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A. Chakrabarti, G. Cormode, A. McGregor, J. Thaler, and S. Venkatasubramanian.
Verifiable stream computation and Arthur-Merlin communication.
In Computational Complexity Conference, 2015.
|
[56]
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Z. Jorgensen, T. Yu, and G. Cormode.
Conservative or liberal? personalized differential privacy.
In International Conference on Data Engineering (ICDE), 2015.
|
[57]
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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.
|
[58]
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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.
|
[59]
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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.
|
[60]
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G. Cormode, S. Muthukrishnan, and J. Yan.
People like us: Mining scholarly data for comparable researchers.
In WWW Workshop on Big Scholarly Data, 2014.
|
[61]
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A. Chakrabarti, G. Cormode, N. Goyal, and J. Thaler.
Annotations for sparse data streams.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2014.
|
[62]
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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.
|
[63]
|
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.
|
[64]
|
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.
|
[65]
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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.
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[66]
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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.
|
[67]
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G. Cormode, K. Mirylenka, T. Palpanas, and D. Srivastava.
Finding interesting correlations with conditional heavy hitters.
In International Conference on Data Engineering (ICDE), 2013.
|
[68]
|
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.
|
[69]
|
G. Cormode and D. Firmani.
On unifying the space of l0-sampling algorithms.
In SIAM Meeting on Algorithm Engineering and Experiments,
2013.
|
[70]
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G. Cormode, J. Thaler, and K. Yi.
Verifying computations with streaming interactive proofs.
In International Conference on Very Large Data Bases (VLDB),
Sept. 2012.
|
[71]
|
A. Goyal, H. Daumé III, and G. Cormode.
Sketch algorithms for estimating point queries in NLP.
In EMNLP-CoNLL, pages 1093-1103, 2012.
|
[72]
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P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi.
Mergeable summaries.
In ACM Principles of Database Systems (PODS), 2012.
|
[73]
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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.
|
[74]
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G. Cormode and K. Yi.
Tracking distributed aggregates over time-based sliding windows.
In Scientific and Statistical Database Management (SSDBM),
2012.
|
[75]
|
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.
|
[76]
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G. Cormode, M. Procopiuc, D. Srivastava, and T. Tran.
Differentially private publication of sparse data.
In International Conference on Database Theory (ICDT), 2012.
|
[77]
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G. Cormode, M. Procopiuc, E. Shen, D. Srivastava, and T. Yu.
Differentially private spatial decompositions.
In International Conference on Data Engineering (ICDE), 2012.
|
[78]
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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.
|
[79]
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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.
|
[80]
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G. Cormode, M. Mitzenmacher, and J. Thaler.
Practical verified computation with streaming interactive proofs.
In Innovations in Theoretical Computer Science (ITCS), 2012.
|
[81]
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P. Agarwal, G. Cormode, Z. Huang, J. Phillips, Z. Wei, and K. Yi.
Mergeable coresets.
In Third Workshop on Massive Data Algorithmics (MASSIVE), 2011.
|
[82]
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G. Cormode.
Personal privacy vs population privacy: Learning to attack
anonymization.
In ACM SIGKDD Conference, 2011.
|
[83]
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E. Cohen, G. Cormode, and N. Duffield.
Structure-aware sampling: Flexible and accurate summarization.
In International Conference on Very Large Data Bases (VLDB),
2011.
|
[84]
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G. Cormode and K. Yi.
Tracking distributed aggregates over time-based sliding windows
(brief announcement).
In ACM Principles of Distributed Computing (PODC), 2011.
|
[85]
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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.
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[86]
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G. Cormode, H. Karloff, and T. Wirth.
Set cover algorithms for very large datasets.
In ACM Conference on Information and Knowledge Management
(CIKM), 2010.
|
[87]
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G. Cormode, S. Muthukrishnan, K. Yi, and Q. Zhang.
Optimal sampling from distributed streams.
In ACM Principles of Database Systems (PODS), 2010.
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[88]
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S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava.
Privacy in dynamic social networks.
In World Wide Web Conference (WWW), 2010.
|
[89]
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G. Cormode, M. Mitzenmacher, and J. Thaler.
Streaming graph computations with a helpful advisor.
In European Symposium on Algorithms, 2010.
|
[90]
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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.
|
[91]
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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.
|
[92]
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S. Bhagat, G. Cormode, B. Krishnamurthy, and D. Srivastava.
Prediction promotes privacy in dynamic social networks.
In Workshop on Online Social Networks (WOSN), 2010.
|
[93]
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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.
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[94]
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G. Cormode, A. Deligiannakis, M. Garofalakis, and A. McGregor.
Probabilistic histograms for probabilistic data.
In International Conference on Very Large Data Bases (VLDB),
2009.
|
[95]
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A. Chakrabarti, G. Cormode, and A. McGregor.
Annotations in data streams.
In International Colloquium on Automata, Languages and
Programming (ICALP), 2009.
|
[96]
|
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.
|
[97]
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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.
|
[98]
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G. Cormode, S. Tirthapura, and B. Xu.
Time-decayed correlated aggregates over data streams.
In SIAM Conference on Data Mining (SDM), 2009.
|
[99]
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G. Cormode and M. Garofalakis.
Histograms and wavelets on probabilistic data.
In International Conference on Data Engineering (ICDE), 2009.
Best paper award.
|
[100]
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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.
|
[101]
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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.
|
[102]
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G. Cormode and M. Hadjieleftheriou.
Finding frequent items in data streams.
In International Conference on Very Large Data Bases (VLDB),
2008.
Best paper award.
|
[103]
|
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.
|
[104]
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G. Cormode, F. Korn, and S. Tirthapura.
Time-decaying aggregates in out-of-order streams.
In ACM Principles of Database Systems (PODS), 2008.
|
[105]
|
G. Cormode and A. McGregor.
Approximation algorithms for clustering uncertain data.
In ACM Principles of Database Systems (PODS), 2008.
|
[106]
|
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.
|
[107]
|
A. Chakrabarti, G. Cormode, and A. McGregor.
Robust lower bounds for communication and stream computation.
In ACM Symposium on Theory of Computing (STOC), 2008.
|
[108]
|
G. Cormode, F. Korn, S. Muthukrishnan, and Y. Wu.
On signatures for communication graphs.
In International Conference on Data Engineering (ICDE), 2008.
|
[109]
|
G. Cormode, F. Korn, and S. Tirthapura.
Exponentially decayed aggregates on data streams.
In International Conference on Data Engineering (ICDE), 2008.
|
[110]
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G. Cormode, S. Muthukrishnan, and K. Yi.
Algorithms for distributed, functional monitoring.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
2008.
|
[111]
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S. Bhagat, G. Cormode, and I. Rozenbaum.
Applying link-based classification to label blogs.
In Joint WEBKDD and SNA-KDD Workshop, 2007.
|
[112]
|
S. Ganguly and G. Cormode.
On estimating frequency moments of data streams.
In Proceedings of RANDOM, 2007.
|
[113]
|
G. Cormode, S. Tirthapura, and B. Xu.
Time-decaying sketches for sensor data aggregation.
In ACM Principles of Distributed Computing (PODC), 2007.
|
[114]
|
G. Cormode and M. Garofalakis.
Sketching probabilistic data streams.
In ACM SIGMOD International Conference on Management of Data
(SIGMOD), 2007.
|
[115]
|
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.
|
[116]
|
G. Cormode, S. Muthukrishnan, and W. Zhuang.
Conquering the divide: Continuous clustering of distributed data
streams.
In International Conference on Data Engineering (ICDE), 2007.
|
[117]
|
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.
|
[118]
|
G. Cormode and S. Muthukrishnan.
Combinatorial algorithms for compressed sensing.
In SIROCCO, 2006.
|
[119]
|
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.
|
[120]
|
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.
|
[121]
|
G. Cormode, M. Garofalakis, and D. Sacharidis.
Fast approximate wavelet tracking on streams.
In Extending Database Technology, pages 4-22, 2006.
|
[122]
|
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.
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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.
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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.
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G. Cormode and S. Muthukrishnan.
Space efficient mining of multigraph streams.
In ACM Principles of Database Systems (PODS), pages
271-282, 2005.
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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.
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G. Cormode and S. Muthukrishnan.
Summarizing and mining skewed data streams.
In SIAM Conference on Data Mining (SDM), 2005.
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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.
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G. Cormode and S. Muthukrishnan.
Substring compression problems.
In ACM-SIAM Symposium on Discrete Algorithms (SODA),
pages 321-330, 2005.
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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.
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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.
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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.
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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.
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G. Cormode and S. Muthukrishnan.
What's new: Finding significant differences in network data streams.
In Proceedings of IEEE Infocom, pages 1534-1545, 2004.
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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.
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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
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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.
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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.
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G. Cormode and S. Muthukrishnan.
Estimating dominance norms of multiple data streams.
In European Symposium on Algorithms, volume 2838 of LNCS,
2003.
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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.
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[141]
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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.
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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.
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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.
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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.
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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.
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