Enhancing Security and Privacy in Augmented Collective Beings

PI: Hsu-Chun Hsiao (National Taiwan University)

Champion: Anand Rajan (Intel), Matthias Schunter (Intel)

Project Objective: 

So far, we have demonstrated our idea works in certain special scenarios. We plan to address our champions’ comments, further validate our idea and work out a feasible defense tool. We have also set up several midterm milestones and final deliverables to check for success at a six-month level. The detailed table is listed below. 

Date

Milestone

Deliverables

Technical Success Criteria/Objectives

2017Q2

Model and detect privacy leak

·         Definition of privacy leak in the context of trigger-action rules

·         Algorithm to detect privacy leak

·         The detection algorithm should be able to accurately identify privacy leak and quantify them to show different levels of warnings.

2017Q4

Develop at least one technique for offline defense; e.g., patching the system by adding or removing rules/devices.

·         Presentation of the offline defense techniques

·         The defense should be able to fix a large percentage of the vulnerabilities while posing little impact on the normal functionalities

2018Q2

Develop at least one technique for online defense; e.g., using a gateway to perform runtime monitoring and filtering

·         Presentation of the offline defense techniques

·         Technical report of the design of the defense module

·         The defense should be able to mitigate a large percentage of the vulnerabilities with low detection delay while posing little impact on the normal functionalities

2018Q4

·         System integration; The 1st version of defense system against multi-stage attacks

·         Study privacy-preserving protocols for rule-based automation.

Prototype of the 1st version of defense system against multi-stage attacks

A detailed technical report with evaluation and prototype implementation 

(updated in Feb, 2017)