At some time between the morning of Jan. 11 and the evening of Jan 13, ChatGPT's prompt was modified again without public notice of an update.
SECURITY
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Data Sovereignty and Cloud Cost Optimization for Enterprise
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Catch our on-demand session where top experts—including Vikash Varma, Global Head of Cloud Program Office at Johnson & Johnson—weigh in on how to protect #datasovereignty, reduce #cloudcosts, and future-proof your infrastructure investments. https://
domino.buzz/3GC3Vxf #MLOps -
SANS Report Reveals ICS/OT Cybersecurity Landscape 2022
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The SANS’ ‘State of ICS/OT Cybersecurity in 2022 and Beyond’ report presents the responses of 332 ICS/OT organizations representing a range of industrial verticals across the ICS/OT community. Check it out! http://
ow.ly/4m1t50Mq957 @ptrancyber #sponsored #xona_ics #cybersecurity -
Data Governance Policy: Quality, Privacy, Security and AI
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"At its core, a data #governance policy shapes protocols around data quality, #privacy, and security; it leverages #AI and streamlines regulatory compliance." #IBM Global Chief Data Officer Inderpal Bhandari on @CarruthersJacks 2022 Data Maturity Index: https://t.co/kOAZVXfww5 pic.twitter.com/LEu9qMSmPg
— IBM Data, AI & Automation (@IBMData) 13 janvier 2023"At its core, a data #governance policy shapes protocols around data quality, #privacy, and security; it leverages #AI and streamlines regulatory compliance." #IBM Global Chief Data Officer Inderpal Bhandari on @CarruthersJacks 2022 Data Maturity Index: https://
ibm.co/3QEuK8T -
Camouflage Poisoning Attack on Machine Learning Models
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For more details, please check out the paper and the code! And definitely remember the names of the first authors, Waterloo undergrads Jimmy Di (applied to grad schools this year) and Jack Douglas. https://
arxiv.org/abs/2212.10717 https://
github.com/Jimmy-di/camou
flage-poisoning
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Defending Against Data Poisoning Through Strategic Point Addition
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From a technical perspective, we raise an interesting new question. Usually, one mitigates data poisoning attacks by *removing* training points. But camouflage essentially asks: can you negate data poisoning attacks by *adding* points! Interesting beyond MU. 7/n
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Adversarial Attacks on Machine Learning Model Updates Research
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We were inspired by previous papers in the "attacking MU" space. See https://
arxiv.org/abs/2109.08266 by Marchant @bipr @ScottAlfeld
, which poisons MU to make it take longer. 8/n -
Dataset Poisoning Attack via Malicious Machine Unlearning
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Here's how it works. The adversary first poisons the dataset, and the victim trains a model on it. Everything behaves as normal. But the adversary later requests some of their points to be unlearned. Only after the unlearning, then the model behaves in some malicious way. 4/n
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Targeted Poisoning Attacks in Machine Learning Models
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In this work we focus on targeted poisoning attacks (the goal is to misclassify a specific point in the test set) & used this attack (
https://
arxiv.org/abs/2009.02276) ft @jonasgeiping @wronnyhuang @tomgoldsteincs
. But ours is a proof of concept, neither is intrinsic to the framework. 5/n -
Camouflage Data Poisoning Attacks in ML Unlearning Systems
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But when you add something new to an ML pipeline, there's new ways for adversaries to wreak havoc. We introduce a new type of data poisoning attack exploiting the dynamic nature of unlearn requests: a "camouflage" attack, which lies dormant until triggered by the adversary. 3/n