# privacy related
@inproceedings {jayaraman2019evaluating,
author = {Bargav Jayaraman and David Evans},
title = {{Evaluating Differentially Private Machine Learning in Practice}},
booktitle = {28th {USENIX} Security Symposium ({USENIX} Security 19)},
year = {2019},
pages = {1895--1912},
publisher = {{USENIX} Association},
}

@inproceedings{abadi2016deep,
  title={{Deep Learning with Differential Privacy}},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
  pages={308--318},
  year={2016}
}
@article{jha2020extension,
  title={{An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks}},
  author={Jha, Sumit Kumar and Jha, Susmit and Ewetz, Rickard and Raj, Sunny and Velasquez, Alvaro and Pullum, Laura L and Swami, Ananthram},
  journal={arXiv preprint arXiv:2009.08097},
  year={2020}
}
@article{laud2019interpreting,
  title={{Interpreting Epsilon of Differential Privacy in Terms of Advantage
               in Guessing or Approximating Sensitive Attributes}},
  author={Laud, Peeter and Pankova, Alisa},
  journal={arXiv preprint arXiv:1911.12777},
  year={2019}
}

@article{dwork2014algorithmic,
year = {2014},
volume = {9},
journal = {{Foundations and Trends\textsuperscript\textregistered in Theoretical Computer Science}},
title = {{The Algorithmic Foundations of Differential Privacy}},
doi = {10.1561/0400000042},
issn = {1551-305X},
number = {3–4},
pages = {211-407},
author = {Cynthia Dwork and Aaron Roth}
}

# mia related
@article{choo2020label,
  title={{Label-Only Membership Inference Attacks}},
  author={Choo, Christopher A Choquette and Tramer, Florian and Carlini, Nicholas and Papernot, Nicolas},
  journal={arXiv preprint arXiv:2007.14321},
  year={2020}
}

@inproceedings{nasr2018machine,
  title={{Machine Learning with Membership Privacy using Adversarial Regularization}},
  author={Nasr, Milad and Shokri, Reza and Houmansadr, Amir},
  booktitle={Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security},
  pages={634--646},
  year={2018}
}

@inproceedings{yeom2018privacy,
  title={Privacy risk in machine learning: Analyzing the connection to overfitting},
  author={Yeom, Samuel and Giacomelli, Irene and Fredrikson, Matt and Jha, Somesh},
  booktitle={2018 IEEE 31st Computer Security Foundations Symposium (CSF)},
  pages={268--282},
  year={2018},
  organization={IEEE}
}

@article{jayaraman2020revisiting,
  title={Revisiting membership inference under realistic assumptions},
  author={Jayaraman, Bargav and Wang, Lingxiao and Evans, David and Gu, Quanquan},
  journal={arXiv preprint arXiv:2005.10881},
  year={2020}
}

@article{song2020systematic,
  title={{Systematic Evaluation of Privacy Risks of Machine Learning Models}},
  author={Song, Liwei and Mittal, Prateek},
  journal={arXiv preprint arXiv:2003.10595},
  year={2020}
}

@inproceedings{shokri2017,
  title={{Membership Inference Attacks Against Machine Learning Models}},
  author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly},
  booktitle={2017 IEEE Symposium on Security and Privacy (SP)},
  pages={3-18},
  year={2017},
}

@article{truex2018towards,
  title={{Towards Demystifying Membership Inference Attacks}},
  author={Truex, Stacey and Liu, Ling and Gursoy, Mehmet Emre and Yu, Lei and Wei, Wenqi},
  journal={arXiv preprint arXiv:1807.09173},
  year={2018}
}

@inproceedings{leino2020stolen,
  title={{Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference}},
  author={Leino, Klas and Fredrikson, Matt},
  booktitle={29th $\{$USENIX$\}$ Security Symposium ($\{$USENIX$\}$ Security 20)},
  pages={1605--1622},
  year={2020}
}

@article{pyrgelis2017knock,
  author    = {Apostolos Pyrgelis and
               Carmela Troncoso and
               Emiliano De Cristofaro},
  title     = {{Knock Knock, Who's There? Membership Inference on Aggregate Location Data}},
  journal   = {CoRR},
  volume    = {abs/1708.06145},
  year      = {2017},
  archivePrefix = {arXiv},
  eprint    = {1708.06145},
  timestamp = {Mon, 13 Aug 2018 16:46:30 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1708-06145.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{nasr2019,
  author={M. {Nasr} and R. {Shokri} and A. {Houmansadr}},
  booktitle={2019 IEEE Symposium on Security and Privacy (SP)},
  title={{Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning}},
  year={2019},
  pages={739-753},
}

@inproceedings{salem2019ml,
  title={{ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models}},
  author={Salem, Ahmed and Zhang, Yang and Humbert, Mathias and Fritz, Mario and Backes, Michael},
  booktitle={Network and Distributed Systems Security Symposium 2019},
  year={2019},
  organization={Internet Society}
}

# inference from genetic data
@article{homer2008resolving,
  title={{Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays}},
  author={Homer, Nils and Szelinger, Szabolcs and Redman, Margot and Duggan, David and Tembe, Waibhav and Muehling, Jill and Pearson, John V and Stephan, Dietrich A and Nelson, Stanley F and Craig, David W},
  journal={PLoS Genet},
  volume={4},
  number={8},
  pages={e1000167},
  year={2008},
  publisher={Public Library of Science}
}

@article{craig2011assessing,
  title={Assessing and managing risk when sharing aggregate genetic variant data},
  author={Craig, David W and Goor, Robert M and Wang, Zhenyuan and Paschall, Justin and Ostell, Jim and Feolo, Michael and Sherry, Stephen T and Manolio, Teri A},
  journal={Nature Reviews Genetics},
  volume={12},
  number={10},
  pages={730--736},
  year={2011},
  publisher={Nature Publishing Group}
}

#federated learning
@inproceedings{mcmahan2017communication,
  title={{Communication-Efficient Learning of Deep Networks from Decentralized Data}},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial Intelligence and Statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@InProceedings{stripelis2021scaling,
  author = 	 {Dimitris Stripelis and Jos\'{e} Luis Ambite and Pradeep Lam and Paul Thompson},
  title = 	    {{Scaling Neuroscience Research using Federated Learning}},
  booktitle =   {IEEE International Symposium on Biomedical Imaging (ISBI)},
  year =        {2021},
}


@article{stripelis2021scalingarxiv,
  title={Scaling Neuroscience Research using Federated Learning},
  author={Stripelis, Dimitris  and Ambite, Jos\'{e} Luis and Lam, Pradeep K. and Thompson, Paul},
  journal={arXiv:2102.},
  year={2021}
}

@article{yang2019federated,
  title={{Federated Machine Learning: Concept and Applications}},
  author={Yang, Qiang and Liu, Yang and Chen, Tianjian and Tong, Yongxin},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={10},
  number={2},
  pages={1--19},
  year={2019},
  publisher={ACM New York, NY, USA}
}


@article{lee2018privacy,
  title={{Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis}},
  author={Lee, Junghye and Sun, Jimeng and Wang, Fei and Wang, Shuang and Jun, Chi-Hyuck and Jiang, Xiaoqian},
  journal={JMIR medical informatics},
  volume={6},
  number={2},
  pages={e20},
  year={2018},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

@inproceedings{sheller2018multi,
  title={{Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation}},
  author={Sheller, Micah J and Reina, G Anthony and Edwards, Brandon and Martin, Jason and Bakas, Spyridon},
  booktitle={International MICCAI Brainlesion Workshop},
  pages={92--104},
  year={2018},
  organization={Springer}
}

@inproceedings{silva2019federated,
  title={{Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data}},
  author={Silva, Santiago and Gutman, Boris A and Romero, Eduardo and Thompson, Paul M and Altmann, Andre and Lorenzi, Marco},
  booktitle={2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019)},
  pages={270--274},
  year={2019},
  organization={IEEE}
}

@article{rieke2020future,
  title={The future of digital health with federated learning},
  author={Rieke, Nicola and Hancox, Jonny and Li, Wenqi and Milletari, Fausto and Roth, Holger and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N and Landman, Bennett and Maier-Hein, Klaus and others},
  journal={npj Digital Medicine},
  volume={3},
  number={119},
  year={2020}
}

@incollection{silva2020fed,
  title={{Fed-BioMed: {A} General Open-Source Frontend Framework for Federated
               Learning in Healthcare}},
  author={Silva, Santiago and Altmann, Andre and Gutman, Boris and Lorenzi, Marco},
  booktitle={Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning},
  pages={201--210},
  year={2020},
  publisher={Springer}
}

@article{bellavista2021decentralised,
  title={{Decentralised Learning in Federated Deployment Environments: A System-Level Survey}},
  author={Bellavista, Paolo and Foschini, Luca and Mora, Alessio},
  journal={ACM Computing Surveys (CSUR)},
  volume={54},
  number={1},
  pages={1--38},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@inproceedings{bonawitz2019towards,
 author = {Bonawitz, Keith and Eichner, Hubert and Grieskamp, Wolfgang and Huba, Dzmitry and Ingerman, Alex and Ivanov, Vladimir and Kiddon, Chlo\'{e} and Kone\v{c}n\'{y}, Jakub and Mazzocchi, Stefano and McMahan, Brendan and Van Overveldt, Timon and Petrou, David and Ramage, Daniel and Roselander, Jason},
 booktitle = {Proceedings of Machine Learning and Systems},
 editor = {A. Talwalkar and V. Smith and M. Zaharia},
 pages = {374--388},
 title = {Towards Federated Learning at Scale: System Design},
 volume = {1},
 year = {2019}
}



@article{MAL-083,
year = {2021},
volume = {14},
journal = {Foundations and Trends® in Machine Learning},
title = {{Advances and Open Problems in Federated Learning}},
doi = {10.1561/2200000083},
issn = {1935-8237},
number = {1},
pages = {-},
author = {Kairouz, Peter and McMahan, H. Brendan}
}




@InProceedings{li2019privacy,
author="Li, Wenqi and Milletar{\`i}, Fausto and Xu, Daguang and Rieke, Nicola and Hancox, Jonny and Zhu, Wentao and Baust, Maximilian and Cheng, Yan and Ourselin, S{\'e}bastien and Cardoso, M. Jorge and Feng, Andrew", 
editor="Suk, Heung-Il and Liu, Mingxia and Yan, Pingkun and Lian, Chunfeng",
title="{{Privacy-Preserving Federated Brain Tumour Segmentation}}",
booktitle="Machine Learning in Medical Imaging",
year="2019",
publisher="Springer International Publishing",
pages="133--141",
}



@article{roy2019braintorrent,
  title={{BrainTorrent: {A} Peer-to-Peer Environment for Decentralized Federated}},
  author={Roy, Abhijit Guha and Siddiqui, Shayan and P{\"o}lsterl, Sebastian and Navab, Nassir and Wachinger, Christian},
  journal={arXiv preprint arXiv:1905.06731},
  year={2019}
}


@article{li2020multi,
title = {{Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results}},
journal = {Medical Image Analysis},
volume = {65},
pages = {101765},
year = {2020},
issn = {1361-8415},
author = {Xiaoxiao Li and Yufeng Gu and Nicha Dvornek and Lawrence H. Staib and Pamela Ventola and James S. Duncan},
}

# brain age related

@article{MRISignBrainAge,
  title={{MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide}},
  author={Bashyam, Vishnu M and Erus, Guray and Doshi, Jimit and Habes, Mohamad and Nasrallah, Ilya M and Truelove-Hill, Monica and Srinivasan, Dhivya and Mamourian, Liz and Pomponio, Raymond and Fan, Yong and others},
  journal={Brain},
  volume={143},
  number={7},
  pages={2312--2324},
  year={2020},
  publisher={Oxford University Press}
}


@article{FENG202015,
  title = {Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging},
  journal = {Neurobiology of Aging},
  volume = {91},
  pages = {15-25},
  year = {2020},
  issn = {0197-4580},
  author = {Xinyang Feng and Zachary C. Lipton and Jie Yang and Scott A. Small and Frank A. Provenzano},
}

@article{cole2017predicting,
  title={Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker},
  author={Cole, James H and Poudel, Rudra PK and Tsagkrasoulis, Dimosthenis and Caan, Matthan WA and Steves, Claire and Spector, Tim D and Montana, Giovanni},
  journal={NeuroImage},
  volume={163},
  pages={115--124},
  year={2017},
  publisher={Elsevier}
}


@InProceedings{gupta2021improved,
   title={{Improved Brain Age Estimation with Slice-based Set Networks}},
  author={Gupta, Umang and Lam, Pradeep and Steeg, Greg Ver and Thompson, Paul},
  booktitle =   {IEEE International Symposium on Biomedical Imaging (ISBI)},
  year =        {2021},
}

@inproceedings{lam2020accurate,
  title={{Accurate brain age prediction using recurrent slice-based networks}},
  author={Lam, Pradeep K and Santhalingam, Vigneshwaran and Suresh, Parth and Baboota, Rahul and Zhu, Alyssa H and Thomopoulos, Sophia I and Jahanshad, Neda and Thompson, Paul M},
  booktitle={16th International Symposium on Medical Information Processing and Analysis},
  year={2020},
  organization={International Society for Optics and Photonics}
}

@article{TenYearsBrainAge,
  author = {Franke, Katja and Gaser, Christian},
  issn = {1664-2295},
  journal = {Frontiers in Neurology},
  pages = {789},
  title = {{Ten Years of BrainAGE as a Neuroimaging Biomarker of Brain Aging: What Insights Have We Gained?}},
  volume = {10},
  year = {2019}
}


@article{peng2021accurate,
  title={Accurate brain age prediction with lightweight deep neural networks},
  author={Peng, Han and Gong, Weikang and Beckmann, Christian F and Vedaldi, Andrea and Smith, Stephen M},
  journal={Medical Image Analysis},
  volume={68},
  pages={101871},
  year={2021},
  publisher={Elsevier}
}

@article{butler2020statistical,
  title={{Statistical Pitfalls in Brain Age Analyses}},
  author={Butler, Ellyn R and Chen, Andrew and Ramadan, Rabie and Le, Trang T and Ruparel, Kosha and Moore, Tyler M and Satterthwaite, Theodore D and Zhang, Fengqing and Shou, Haochang and Gur, Ruben C and others},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}
@article{vidal2021brain,
  title = {{{\textquotedblleft}Brain age{\textquotedblright} relates to early life factors but not to accelerated brain aging}},
  author={Vidal-Pineiro, Didac and Wang, Yunpeng and Krogsrud, Stine K and Amlien, Inge K and Baare, William FC and Bartres-Faz, David and Bertram, Lars and Brandmaier, Andreas M and Drevon, Christian A and Duzel, Sandra and others},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}

@article{jonsson2019brain,
  title={Brain age prediction using deep learning uncovers associated sequence variants},
  author={J{\'o}nsson, Benedikt Atli and Bjornsdottir, Gyda and Thorgeirsson, TE and Ellingsen, Lotta Mar{\'\i}a and Walters, G Bragi and Gudbjartsson, DF and Stefansson, Hreinn and Stefansson, Kari and Ulfarsson, MO},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1--10},
  year={2019},
  publisher={Nature Publishing Group}
}

# Law related
@misc{hipaa,
  added-at = {2009-01-31T03:43:30.000+0100},
  author = {{Centers for Medicare \& Medicaid Services}},
  biburl = {https://www.bibsonomy.org/bibtex/2c762ed125b77a08edb1a3998f25a7a8b/ragibhasan},
  howpublished = {Online at http://www.cms.hhs.gov/hipaa/},
  interhash = {4c0dfb55ab4a787637b71d40cd843c72},
  intrahash = {c762ed125b77a08edb1a3998f25a7a8b},
  keywords = {hipaa law},
  timestamp = {2009-01-31T03:47:33.000+0100},
  title = {{The Health Insurance Portability and Accountability Act of 1996 (HIPAA)}},
  year = 1996
}

# defacing and other misc refs
@inproceedings{schimke2011quickshear,
  title={{Quickshear Defacing for Neuroimages}},
  author={Schimke, Nakeisha and Hale, John},
  booktitle={Proceedings of the 2nd USENIX conference on Health security and privacy},
  pages={11--11},
  year={2011},
  organization={USENIX Association}
}

@article{milchenko2013obscuring,
  title={{Obscuring Surface Anatomy in Volumetric Imaging Data
}},
  author={Milchenko, Mikhail and Marcus, Daniel},
  journal={Neuroinformatics},
  volume={11},
  number={1},
  pages={65--75},
  year={2013},
  publisher={Springer}
}

@article{bischoff2007technique,
  title={{A technique for the deidentification of structural brain MR images}},
  author={Bischoff-Grethe, Amanda and Ozyurt, I Burak and Busa, Evelina and Quinn, Brian T and Fennema-Notestine, Christine and Clark, Camellia P and Morris, Shaunna and Bondi, Mark W and Jernigan, Terry L and Dale, Anders M and others},
  journal={Human brain mapping},
  volume={28},
  number={9},
  pages={892--903},
  year={2007},
  publisher={Wiley Online Library}
}

@inproceedings{wegmayr2018classification,
  title={Classification of brain MRI with big data and deep 3D convolutional neural networks},
  author={Wegmayr, Viktor and Aitharaju, Sai and Buhmann, Joachim},
  booktitle={Medical Imaging 2018: Computer-Aided Diagnosis},
  volume={10575},
  pages={105751S},
  year={2018},
  organization={International Society for Optics and Photonics}
}

@inproceedings{hon2017towards,
  title={Towards Alzheimer's disease classification through transfer learning},
  author={Hon, Marcia and Khan, Naimul Mefraz},
  booktitle={2017 IEEE International conference on bioinformatics and biomedicine (BIBM)},
  pages={1166--1169},
  year={2017},
  organization={IEEE}
}

@article{ukbb,
  title={{Multimodal population brain imaging in the UK Biobank prospective epidemiological study}},
  author={Miller, Karla L and Alfaro-Almagro, Fidel and Bangerter, Neal K and Thomas, David L and Yacoub, Essa and Xu, Junqian and Bartsch, Andreas J and Jbabdi, Saad and Sotiropoulos, Stamatios N and Andersson, Jesper LR and others},
  journal={Nature neuroscience},
  volume={19},
  number={11},
  pages={1523--1536},
  year={2016},
  publisher={Nature Publishing Group}
}


@inproceedings{zhang2017rethinking,
  title={{Understanding deep learning requires rethinking generalization}},
  author={Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
  booktitle={International Conference on Learning Representations},
  year={2017},
}
