<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>jaster</author><text>Fun coincidence (or maybe not?), I just stumbled upon the following paper on arvix titled &amp;quot;Differentially Private Generative Adversarial Network&amp;quot; &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1802.06739v1.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1802.06739v1.pdf&lt;/a&gt;&lt;p&gt;Its Related Work section also references some works on &amp;quot;differentially private learning in neural network&amp;quot;.&lt;p&gt;While the paper does not discuss directly OP remark about how differential privacy could help generality in machine learning, it shows that at least some connections exist between the domains.</text><parent_chain><item><author>lalaland1125</author><text>One very interesting thing about differential privacy is its connection to generalizability in machine learning. In particular, a model can only provide differential privacy if that model is completely generalizable. Generalizability is the ability of a model to do well on data outside its training set. Non generalized models violate differential privacy because you can find out if someone is in the training set by simply measuring the error rate on that person. A low error rate provides evidence that that particular person was in the training set.&lt;p&gt;This connection is interesting because it implies that differential privacy techniques applied to machine learning must in turn result in more generalizable models, which is very useful in general. This can also provide a simple &amp;quot;upper bound&amp;quot; on how differentialy private your model is. Simply compare the performance on the training and test set and see how much it differs.</text></item></parent_chain></comment><story><title>Differential Privacy for Dummies (2016)</title><url>https://robertovitillo.com/2016/07/29/differential-privacy-for-dummies/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>maksimum</author><text>&amp;gt; This can also provide a simple &amp;quot;upper bound&amp;quot; on how differentialy private your model is.&lt;p&gt;This is a very interesting idea. Are you aware of any resources that go through the mechanics?</text><parent_chain><item><author>lalaland1125</author><text>One very interesting thing about differential privacy is its connection to generalizability in machine learning. In particular, a model can only provide differential privacy if that model is completely generalizable. Generalizability is the ability of a model to do well on data outside its training set. Non generalized models violate differential privacy because you can find out if someone is in the training set by simply measuring the error rate on that person. A low error rate provides evidence that that particular person was in the training set.&lt;p&gt;This connection is interesting because it implies that differential privacy techniques applied to machine learning must in turn result in more generalizable models, which is very useful in general. This can also provide a simple &amp;quot;upper bound&amp;quot; on how differentialy private your model is. Simply compare the performance on the training and test set and see how much it differs.</text></item></parent_chain></comment><story><title>Differential Privacy for Dummies (2016)</title><url>https://robertovitillo.com/2016/07/29/differential-privacy-for-dummies/</url></story>