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A learned database system uses machine learning (ML) internally to improve performance.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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On the importance of checking cryptographic protocols for faults
Dan Boneh, Richard A DeMillo, and Richard J Lipton · 1997
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Jflow: Practical mostly-static information flow control
Andrew C Myers · 1999
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Humpty dumpty: Controlling word meanings via corpus poisoning
Roei Schuster, Tal Schuster, Yoav Meri, and Vitaly Shmatikov · 2001
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Secure program partitioning
Steve Zdancewic, Lantian Zheng, Nathaniel Nystrom, and Andrew C Myers · 2002
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Denial of service via algorithmic complexity attacks
Scott A Crosby and Dan S Wallach · 2003
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An intelligent framework for predicting shifts in the workloads of autonomic database management systems
Said S Elnaffar and Patrick Martin · 2004
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Imitation attacks and defenses for black-box machine translation systems
Eric Wallace, Mitchell Stern, and Dawn Song · 2004
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Challenges for information-flow security
Steve Zdancewic · 2004
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Tsunami: A learned multi-dimensional index for correlated data and skewed workloads
Jialin Ding, Vikram Nathan, Mohammad Alizadeh, and Tim Kraska · 2006
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Cache attacks and countermeasures: the case of aes
Dag Arne Osvik, Adi Shamir, and Eran Tromer · 2006
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The nd2db attack: Database content extraction using timing attacks on the indexing algorithms
Ariel Futoransky, Damián Saura, and Ariel Waissbein · 2007
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Information flow control for standard os abstractions
Maxwell Krohn, Alexander Yip, Micah Brodsky, Natan Cliffer, M Frans Kaashoek, Eddie Kohler, and Robert Morris · 2007
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You autocomplete me: Poisoning vulnerabilities in neural code completion
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov · 2007
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Consistent on-line classification of dbs workload events
Marc Holze, Claas Gaidies, and Norbert Ritter · 2009
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Benchmarking cloud serving systems with ycsb
Brian F Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears · 2010
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Towards workload-aware self-management: Predicting significant workload shifts
Marc Holze, Ali Haschimi, and Norbert Ritter · 2010
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Efficient cache attacks on aes, and countermeasures
Eran Tromer, Dag Arne Osvik, and Adi Shamir · 2010
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Customizing triggers with concealed data poisoning
Eric Wallace, Tony Z Zhao, Shi Feng, and Sameer Singh · 2010
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Google c++ b-tree
B-tree · 2011
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Hash collision dos attacks. 28c3, 2011
J Wälde and A Klink · 2011
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel · 2014
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Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones
William Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N Sheth · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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{ \{ FLUSH+ RELOAD } \} : A high resolution, low noise, l3 cache { \{ Side-Channel } \} attack
Yuval Yarom and Katrina Falkner · 2014
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
How good are query optimizers, really?
Viktor Leis, Andrey Gubichev, Atanas Mirchev, Peter Boncz, Alfons Kemper, and Thomas Neumann · 2015
Cited alongside, same era.
Hey, you have given me too many knobs!: Understanding and dealing with over-designed configuration in system software
Tianyin Xu, Long Jin, Xuepeng Fan, Yuanyuan Zhou, Shankar Pasupathy, and Rukma Talwadker · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Rowhammer. js: A remote software-induced fault attack in javascript
Daniel Gruss, Clémentine Maurice, and Stefan Mangard · 2016
Defending neural backdoors via generative distribution modeling
Ximing Qiao, Yukun Yang, and Hai Li · 2019
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Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov · 2019
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Model agnostic defence against backdoor attacks in machine learning
Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, and Sudipta Chattopadhyay · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
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Detecting AI trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2019
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Cited alongside, same era.
Resource management with deep reinforcement learning
Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Cited alongside, same era.
Approximate query processing: No silver bullet
Surajit Chaudhuri, Bolin Ding, and Srikanth Kandula · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Automatic database management system tuning through large-scale machine learning
Dana Van Aken, Andrew Pavlo, Geoffrey J Gordon, and Bohan Zhang · 2017
Cited alongside, same era.
Blackbox attacks on reinforcement learning agents using approximated temporal information, 2019
Yiren Zhao, Ilia Shumailov, Han Cui, Xitong Gao, Robert Mullins, and Ross Anderson · 2019
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Learned indexes for a google-scale disk-based database
Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H Chi, Lyric Doshi, Tim Kraska, Andy Ly, Christopher Olston, et al · 2020
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Blind backdoors in deep learning models
Eugene Bagdasaryan and Vitaly Shmatikov · 2020
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Hopskipjumpattack: A query-efficient decision-based attack
Jianbo Chen, Michael I Jordan, and Martin J Wainwright · 2020
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Alex: an updatable adaptive learned index
Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, et al · 2020
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The pgm-index: a fully-dynamic compressed learned index with provable worst-case bounds
Paolo Ferragina and Giorgio Vinciguerra · 2020
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Too many knobs to tune? towards faster database tuning by pre-selecting important knobs
Konstantinos Kanellis, Ramnatthan Alagappan, and Shivaram Venkataraman · 2020
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The price of tailoring the index to your data: Poisoning attacks on learned index structures, 2020
Evgenios M. Kornaropoulos, Silei Ren, and Roberto Tamassia · 2020
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Weight poisoning attacks on pre-trained models
Keita Kurita, Paul Michel, and Graham Neubig · 2020
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Learning multi-dimensional indexes
Vikram Nathan, Jialin Ding, Mohammad Alizadeh, and Tim Kraska · 2020
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Quicksel: Quick selectivity learning with mixture models
Yongjoo Park, Shucheng Zhong, and Barzan Mozafari · 2020
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Fast rdma-based ordered key-value store using remote learned cache
Xingda Wei, Rong Chen, and Haibo Chen · 2020
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Qd-tree: Learning data layouts for big data analytics
Zongheng Yang, Badrish Chandramouli, Chi Wang, Johannes Gehrke, Yinan Li, Umar Farooq Minhas, Per-Åke Larson, Donald Kossmann, and Rajeev Acharya · 2020
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Steering query optimizers: A practical take on big data workloads
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, and Alekh Jindal · 2021
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Bao: Making learned query optimization practical
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska · 2022
Closest in time.
"real attackers don’t compute gradients": Bridging the gap between adversarial ML research and practice
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, and Kevin A. Roundy · 2023
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Algorithmic complexity attacks on dynamic learned indexes
Rui Yang, Evgenios M. Kornaropoulos, and Yue Cheng · 2023
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Privacy side channels in machine learning systems
Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, and Florian Tramèr · 2024
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Permissions (database engine) — sql server | microsoft docs
MSSQL · 2024
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Cardinality estimation using normalizing flow
Jiayi Wang, Chengliang Chai, Jiabin Liu, and Guoliang Li · 2024
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