Secure Analytics on the Cloud

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Privacy and confidentiality concerns have made some organizations reluctant to take advantage of large scale analytics on the cloud. In addition to regulations that limit the disclosure of personal information, there are legitimate concerns about potential data leaks, compelled disclosures, and litigation. Secure computation is a set of techniques for sharing encrypted data with cloud providers, and then performing the analytics on the encrypted data. The final encrpyted results are then returned to the customer who decrypts it to get the final results. A special type of cryptography, known as homomorphic encryption, is used in these situations and which allows mathematical operations to be performed on the ciphertext. Protocols can be constructed to implement quite complex modeling algorithms using these methods.

Example scenarios where secure computation in the cloud can be used include: a health researcher or pharmaceutical company sharing their data with the broader research community by posting the encrypted data on-line and still permitting analytics on that data, a financial services company wants to take advantage of the elastic computing capacity on the cloud without having to trust the cloud provider with its sensitive data, and a wellness company collecting personal information directly from consumers either on-line or through wearable devices and performing analytics on their data for benchmarking purposes without knowing the exact values collected from the consumers.

In this presentation I will give an overview of secure computation, how it protects privacy and confidentiality, and its limitations. I will then give some real-world examples of how it has been used in the healthcare sector. These examples include public health surveillance, sharing clinical research data, and securely tracking individuals as they visit multiple care facilities.

Photo of Khaled El Emam

Khaled El Emam

Children's Hospital of Eastern Ontario - Research Institute & University of Ottawa

Dr. Khaled El Emam is the Founder and CEO of Privacy Analytics, Inc. He is also an Associate Professor at the University of Ottawa, Faculty of Medicine, a senior investigator at the Children’s Hospital of Eastern Ontario Research Institute, and a Canada Research Chair in Electronic Health Information at the University of Ottawa. His main area of research is developing techniques for health data de-identification or anonymization and secure disease surveillance for public health purposes. He has made many contributions to the health privacy area. In addition, he has considerable experience de-identifying personal health information under the HIPAA Privacy Rule Statistical Standard.

Previously Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. He has co-founded two companies to commercialize the results of his research work. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement, and ranked second in 2002 and 2005. He holds a PhD from the Department of Electrical and Electronics Engineering, King’s College, at the University of London (UK). His website is


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