Copyright: the listed software tools and datasets are free for non-commercial use. The authors have no responsibility or legal obligation for any consequence caused by using them. If you want to use them in your publications, please cite them appropriately. Please contact Keke Chen for any further questions.

• Disguising Images: comparing private image instance encoding methods that can utilize  GPUs in confidential deep learning

Please cite: Keke Chen, Yuechun Gu, and Sagar Sharma, "DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds", ACM Transactions on Internet Technology, volume 23, issue 3, 2023

• SGX-MR: regulating application data flow with MapReduce for efficiently protecting from access pattern leakage of data-intensive SGX applications (the binary release is available)

Please cite Mubashwir Alam, Sagar Sharma, and Keke Chen, "SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications", Proceedings of Privacy Enhancing Technologies Symposium (PETS), Sydney,  Australia, 2021

• PrivateGraph: Privacy-Preserving Graph Spectral Analysis for Encrypted Graphs in the Cloud. A simple cloud-client demo (Using gmplib, flask, appjar)

Please cite Sagar Sharma, James Powers, and Keke Chen, "Privacy-Preserving Spectral Analysis of Large Graphs in Public Clouds", ACM Symposium on InformAtion, Computer and Communications Security (ASIACCS), Xi'an, China, 2016

• CRESP optimizer for MapReduce programs in public clouds (scripts for model training)

Please cite Keke Chen, James Powers, Shumin Guo, and Fengguang Tian: "CRESP: Towards Optimal Resource Provisioning for MapReduce Computing in Public Clouds ", IEEE Transactions on Parallel and Distributed Systems (TPDS), Volume 25, Number 6, 2014

• Secure and privacy-preserving range query service with RASP perturbation (RASP matlab code, query processing demo and visualization)

Please cite Huiqi Xu, Shumin Guo, and Keke Chen " Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation”, TKDE, 2014

• CloudVista demo system ( Client-Cloud Demo System)

Please cite: Keke Chen, Huiqi Xu, Fengguang Tian, Shumin Guo: " CloudVista: visual cluster exploration for extreme scale data in the cloud ", Scientific and Statistical Database Management Conference, Portland OR, 2011

• User privacy setting data for social network services. Please find the datasets and description.

Please cite Shumin Guo and Keke Chen: "Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services", IEEE/ASE Conference on Privacy, Security, Risk and Trust (PASSAT), Amsterdam, Netherlands, 2012

• Geometric data perturbation – attack evaluation and parameter optimization (version 1.1, with corrected optimization)

Please cite: Keke Chen and Ling Liu: "Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining ", Journal of Knowledge and Information Systems (KAIS), 2011

• BKPlot – finding the best K for categorical data clustering (version 2.0)

Please cite: Keke Chen and Ling Liu: " Best K: the Critical Clustering Structures in Categorical Data ", Knowedge and Information Systems, 2008

• VISTA - interactive visual cluster exploration system: latest version 0.3.2 on gitlab, original website

Please cite: Keke Chen and Ling Liu: "VISTA: Validating and Refining Clusters via Visualization." Journal of Information Visualization, Sept. 2004

Other tools:

• RA-SVM ranking adaptation (Geng et al. CIKM 2009) our implementation based on liblinear SVM. It was used in our ACM TOIS paper