Cyber Defense eMagazine December Edition for 2021
Will you stay one step ahead of Cyber Scrooge this year? Learn new ways to protect your family, job, company & data. December Cyber Defense eMagazine: Cyber Deception Month is here...Defeat Cyber Scrooge! Cyber Defense Magazine December Edition for 2021 in online format #CDM #CYBERDEFENSEMAG @CyberDefenseMag by @Miliefsky a world-renowned cyber security expert and the Publisher of Cyber Defense Magazine as part of the Cyber Defense Media Group as well as Yan Ross, US Editor-in-Chief, Pieruligi Paganini, International Editor-in-Chief and many more writers, partners and supporters who make this an awesome publication! Thank you all and to our readers! OSINT ROCKS! #CDM #CDMG #OSINT #CYBERSECURITY #INFOSEC #BEST #PRACTICES #TIPS #TECHNIQUES See you at RSA Conference 2022 - Our 10th Year Anniversary - Our 10th Year @RSAC #RSACONFERENCE #USA - Thank you so much!!! - Team CDMG CDMG is a Carbon Negative and Inclusive Media Group.
Will you stay one step ahead of Cyber Scrooge this year? Learn new ways to protect your family, job, company & data. December Cyber Defense eMagazine: Cyber Deception Month is here...Defeat Cyber Scrooge!
Cyber Defense Magazine December Edition for 2021 in online format #CDM #CYBERDEFENSEMAG @CyberDefenseMag by @Miliefsky a world-renowned cyber security expert and the Publisher of Cyber Defense Magazine as part of the Cyber Defense Media Group as well as Yan Ross, US Editor-in-Chief, Pieruligi Paganini, International Editor-in-Chief and many more writers, partners and supporters who make this an awesome publication! Thank you all and to our readers! OSINT ROCKS! #CDM #CDMG #OSINT #CYBERSECURITY #INFOSEC #BEST #PRACTICES #TIPS #TECHNIQUES
See you at RSA Conference 2022 - Our 10th Year Anniversary - Our 10th Year @RSAC #RSACONFERENCE #USA - Thank you so much!!! - Team CDMG
CDMG is a Carbon Negative and Inclusive Media Group.
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validation study by Abdalla et al. (2020) [Mohamed Abdalla, Moustafa Abdalla, Graeme Hirst, and<br />
Frank Rudzicz. 2020. Exploring the privacy-preserving properties of word embeddings:<br />
Algorithmic validation study. J Med Internet Res.].<br />
There are four types of disclosure concerns when it comes to protecting data privacy:<br />
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●<br />
●<br />
Identity disclosure: identifying an individual.<br />
Attribute disclosure: identifying an individual’s ethnicity, religion, physical attributes, etc.<br />
Group attribute disclosure: e.g., is a particular group more likely to have cancer?<br />
Membership disclosure: e.g., is this person part of a pharmaceutical trial?<br />
Not all attributes are the same with regards to increasing the risk of these disclosures. Within<br />
conversations with chatbots, users might reveal direct identifiers (e.g., full names, exact addresses,<br />
phone numbers, credit card numbers) and quasi-identifiers (e.g., religion, origin, gender, etc.). When<br />
combining quasi-identifiers together, the risk of re-identifying an individual grows exponentially.<br />
The ScatterLab incident mentioned above is an example of identity and possibly attribute disclosure,<br />
though one major issue was actually membership disclosure through identity disclosure. These<br />
disclosure types were caused by the leak of direct identifiers and perhaps of quasi-identifiers as well.<br />
Preventing Identity, Attribute, and Membership Disclosures<br />
There are a few solutions <strong>for</strong> dealing with training data memorization within chatbots. One is differentially<br />
private gradient descent (DPGD), which was used in Carlini et al.’s 2019 paper. DPGD adds noise to the<br />
ML model training process. The original idea behind differential privacy is to be able to make<br />
generalizations about a population without the risk of disclosing any specific individual’s unique<br />
in<strong>for</strong>mation. The goal of adding differential privacy to an algorithm, like a chatbot model, is that if you run<br />
the algorithm on two datasets differing by a single entry, then the likelihood of getting a different set of<br />
possible outputs is negligible. DPGD provides mathematical guarantees that rare in<strong>for</strong>mation is not being<br />
memorized by a machine learning model, though often at the expense of model utility.<br />
Another solution is highly accurate redaction or de-identification, which means removing the direct<br />
identifiers and quasi-identifiers within your training data (e.g., location, names, telephone numbers, etc.).<br />
There’s a lot you can gather from a conversation’s context without the need <strong>for</strong> identifiable in<strong>for</strong>mation.<br />
Finally, another option is using synthetic personal data generation. This method allows <strong>for</strong> replacement<br />
of direct and quasi-identifiers in a very natural way, so a chatbot’s training data matches the style of the<br />
language model’s pre-training dataset, which prevents downstream model accuracy loss. It also has the<br />
additional benefit that, if any personally identifiable in<strong>for</strong>mation is missed, it’s very difficult to tell what the<br />
original data was from the synthetic data. Targeted synthetic data generation changes the paradigm of<br />
disclosure risk versus data utility.<br />
If ScatterLab had used either one of these three methods to protect the privacy of their users, they would<br />
have prevented violating training data privacy, as well as input and output privacy. Their story inspires<br />
<strong>Cyber</strong> <strong>Defense</strong> <strong>eMagazine</strong> – <strong>December</strong> <strong>2021</strong> <strong>Edition</strong> 106<br />
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