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Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

talks

Lightweight Privacy-Preserving Deep Learning and Inference in Internet of Things

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Abstract: With the rapid development of sensing and communication technologies, the Internet of Things (IoT) is becoming a global data generation infrastructure. To utilize the massive data generated by IoT for achieving better system intelligence, machine learning and inference on the IoT data at the edge and core (i.e., cloud) of the IoT are needed. However, the pervasive data collection and processing engender various privacy concerns. While various privacy preservation mechanisms have been proposed in the context of cloud computing, they may be ill suited for IoT due to the resource constraints at the IoT edge. In this talk, I will present four privacy-preserving approaches on the learning and inference phases. These four approaches are computationally lightweight and can be executed by resource-limited edge devices including smartphones and even mote-class sensor nodes. Extensive performance evaluation performed on multiple datasets and real implementations on IoT hardware platforms show the effectiveness and efficiency of these approaches in protecting data privacy while maintaining the learning and inference performance.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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