Cyber Defense eMagazine December Edition for 2021

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validation study by Abdalla et al. (2020) [Mohamed Abdalla, Moustafa Abdalla, Graeme Hirst, and Frank Rudzicz. 2020. Exploring the privacy-preserving properties of word embeddings: Algorithmic validation study. J Med Internet Res.]. There are four types of disclosure concerns when it comes to protecting data privacy: ● ● ● ● Identity disclosure: identifying an individual. Attribute disclosure: identifying an individual’s ethnicity, religion, physical attributes, etc. Group attribute disclosure: e.g., is a particular group more likely to have cancer? Membership disclosure: e.g., is this person part of a pharmaceutical trial? Not all attributes are the same with regards to increasing the risk of these disclosures. Within conversations with chatbots, users might reveal direct identifiers (e.g., full names, exact addresses, phone numbers, credit card numbers) and quasi-identifiers (e.g., religion, origin, gender, etc.). When combining quasi-identifiers together, the risk of re-identifying an individual grows exponentially. The ScatterLab incident mentioned above is an example of identity and possibly attribute disclosure, though one major issue was actually membership disclosure through identity disclosure. These disclosure types were caused by the leak of direct identifiers and perhaps of quasi-identifiers as well. Preventing Identity, Attribute, and Membership Disclosures There are a few solutions for dealing with training data memorization within chatbots. One is differentially private gradient descent (DPGD), which was used in Carlini et al.’s 2019 paper. DPGD adds noise to the ML model training process. The original idea behind differential privacy is to be able to make generalizations about a population without the risk of disclosing any specific individual’s unique information. The goal of adding differential privacy to an algorithm, like a chatbot model, is that if you run the algorithm on two datasets differing by a single entry, then the likelihood of getting a different set of possible outputs is negligible. DPGD provides mathematical guarantees that rare information is not being memorized by a machine learning model, though often at the expense of model utility. Another solution is highly accurate redaction or de-identification, which means removing the direct identifiers and quasi-identifiers within your training data (e.g., location, names, telephone numbers, etc.). There’s a lot you can gather from a conversation’s context without the need for identifiable information. Finally, another option is using synthetic personal data generation. This method allows for replacement of direct and quasi-identifiers in a very natural way, so a chatbot’s training data matches the style of the language model’s pre-training dataset, which prevents downstream model accuracy loss. It also has the additional benefit that, if any personally identifiable information is missed, it’s very difficult to tell what the original data was from the synthetic data. Targeted synthetic data generation changes the paradigm of disclosure risk versus data utility. If ScatterLab had used either one of these three methods to protect the privacy of their users, they would have prevented violating training data privacy, as well as input and output privacy. Their story inspires Cyber Defense eMagazine – December 2021 Edition 106 Copyright © 2021, Cyber Defense Magazine. All rights reserved worldwide.
concern and a desire to be cautious. Though it is not enough to just want to be proactive about user privacy: actions must be taken to integrate privacy into a chatbot’s very design. About the Author Patricia Thaine is the Co-Founder and CEO of Private AI, a Toronto- and Berlin-based startup creating a suite of privacy tools that make it easy to comply with data protection regulations, mitigate cybersecurity threats, and maintain customer trust. She is a Computer Science PhD Candidate at the University of Toronto and a Postgraduate Affiliate at the Vector Institute doing research on privacy-preserving natural language processing, with a focus on applied cryptography. Her research interests also include computational methods for lost language decipherment. Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice “Trixie” Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She has nine years of research and software development experience, including at the McGill Language Development Lab, the University of Toronto's Computational Linguistics Lab, the University of Toronto's Department of Linguistics, and the Public Health Agency of Canada. She is also a member of the Board of Directors of Equity Showcase, one of Canada's oldest not-for-profit charitable organizations. Patricia Thaine can be reached online at patricia@private-ai.com, @PrivateNLP and at our company website https://www.private-ai.com. Cyber Defense eMagazine – December 2021 Edition 107 Copyright © 2021, Cyber Defense Magazine. All rights reserved worldwide.
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How Do You Secure the Modern Supply
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Cyber Defense eMagazine - December
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CyberDefense.TV now has 200 hotseat
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Books by our Publisher: https://www
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