Security from Implicit Information

Security from Implicit Information. Nguyen, L. N. Ph.D. Thesis, Aalto University, September, 2020.

We present novel security mechanisms using implicit information extracted from physiological, behavioural, and ambient data. These mechanisms are implemented with reference to device-to-user and inter-device relationships, including: user authentication with transient image-based passwords, device-to-device secure connection initialization based on vocal commands, collaborative inference over the communication channel, and continuous on-body device pairing. Authentication methods based on passwords require users to explicitly set their passwords and change them regularly. We introduce a method to generate always-fresh authentication challenges from videos collected by wearable cameras. We implement two password formats that expect users to arrange or select images according to their chronological information. Radio waves are mainly used for data transmission. We implement function computation over the wireless signals to perform collaborative inference. We encode information into burst sequences in such a way that arithmetic functions can be computed using the interference. Hence, data is hidden inside the wireless signals and implicitly aggregated. Our algorithms allow us to train and deploy a classifier efficiently with the support of minimal backscatter devices. To initialize a connection between a personal device (e.g. smart-phone) and shared appliances (e.g. smart-screens), users are required to explicitly ask for connection information including device identities and PIN codes. We propose to leverage natural vocal commands to select shared appliance types and generate secure communication keys from the audio implicitly. We perform experiments to verify that device proximity defined by audio fingerprints can restrict the range of device-to-device communication. PIN codes in device pairing must be manually entered or verified by users. This is inconvenient in scenarios when pairing is performed frequently or devices have limited user interfaces. Our methods generate secure pairing keys for on-body devices continuously from sensor data. Our mechanisms automatically disconnect the devices when they leave the user's body. To cover all human activities, we leverage gait in human ambulatory actions and heartbeat in resting postures.

@PhDThesis{LeThesis2020,
author = "Le Ngu Nguyen",
title = "Security from Implicit Information",
school = "Aalto University",
year = "2020",
month = "September",
isbn = "978-952-64-0013-6",
url_Paper ={https://aaltodoc.aalto.fi/handle/123456789/46392},
abstract = {We present novel security mechanisms using implicit information extracted from physiological, behavioural, and ambient data. These mechanisms are implemented with reference to device-to-user and inter-device relationships, including: user authentication with transient image-based passwords, device-to-device secure connection initialization based on vocal commands, collaborative inference over the communication channel, and continuous on-body device pairing.

Authentication methods based on passwords require users to explicitly set their passwords and change them regularly. We introduce a method to generate always-fresh authentication challenges from videos collected by wearable cameras. We implement two password formats that expect users to arrange or select images according to their chronological information.

Radio waves are mainly used for data transmission. We implement function computation over the wireless signals to perform collaborative inference. We encode information into burst sequences in such a way that arithmetic functions can be computed using the interference. Hence, data is hidden inside the wireless signals and implicitly aggregated. Our algorithms allow us to train and deploy a classifier efficiently with the support of minimal backscatter devices.

To initialize a connection between a personal device (e.g. smart-phone) and shared appliances (e.g. smart-screens), users are required to explicitly ask for connection information including device identities and PIN codes. We propose to leverage natural vocal commands to select shared appliance types and generate secure communication keys from the audio implicitly. We perform experiments to verify that device proximity defined by audio fingerprints can restrict the range of device-to-device communication.

PIN codes in device pairing must be manually entered or verified by users. This is inconvenient in scenarios when pairing is performed frequently or devices have limited user interfaces. Our methods generate secure pairing keys for on-body devices continuously from sensor data. Our mechanisms automatically disconnect the devices when they leave the user's body. To cover all human activities, we leverage gait in human ambulatory actions and heartbeat in resting postures.},
group = {ambience},
project = {abacus}}

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