machine-learning-diff-private-federated-learning | federated setting and run | Privacy library

 by   SAP-samples Python Version: Current License: Apache-2.0

kandi X-RAY | machine-learning-diff-private-federated-learning Summary

kandi X-RAY | machine-learning-diff-private-federated-learning Summary

machine-learning-diff-private-federated-learning is a Python library typically used in Security, Privacy applications. machine-learning-diff-private-federated-learning has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However machine-learning-diff-private-federated-learning build file is not available. You can download it from GitHub.

Simulate a federated setting and run differentially private federated learning.

            kandi-support Support

              machine-learning-diff-private-federated-learning has a low active ecosystem.
              It has 219 star(s) with 54 fork(s). There are 13 watchers for this library.
              It had no major release in the last 6 months.
              machine-learning-diff-private-federated-learning has no issues reported. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of machine-learning-diff-private-federated-learning is current.

            kandi-Quality Quality

              machine-learning-diff-private-federated-learning has 0 bugs and 0 code smells.

            kandi-Security Security

              machine-learning-diff-private-federated-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              machine-learning-diff-private-federated-learning code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              machine-learning-diff-private-federated-learning is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              machine-learning-diff-private-federated-learning releases are not available. You will need to build from source code and install.
              machine-learning-diff-private-federated-learning has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              machine-learning-diff-private-federated-learning saves you 457 person hours of effort in developing the same functionality from scratch.
              It has 1080 lines of code, 83 functions and 9 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed machine-learning-diff-private-federated-learning and discovered the below as its top functions. This is intended to give you an instant insight into machine-learning-diff-private-federated-learning implemented functionality, and help decide if they suit your requirements.
            • Generate a sample of the model
            • Clipped updates
            • Update the model using Gaussian
            • Runs multiple adversarial accounts
            • Compute the privacy spent in a given session
            • Compute the minimum steps of log_moments
            • Compute the minimum difference between the log moments
            • Create MNIST convolutional model
            • Compute the evaluation of the given labels
            • Create placeholder inputs
            • Compute the logarithm of the binomial
            • Calculate differential moments
            • Summarize the policies for the policy
            • Compute the log moment
            • Create numclients
            Get all kandi verified functions for this library.

            machine-learning-diff-private-federated-learning Key Features

            No Key Features are available at this moment for machine-learning-diff-private-federated-learning.

            machine-learning-diff-private-federated-learning Examples and Code Snippets

            No Code Snippets are available at this moment for machine-learning-diff-private-federated-learning.

            Community Discussions


            De-identifying survival or flexsurvreg objects in R
            Asked 2022-Mar-29 at 11:01

            Please consider the following:

            I need to provide some R code syntax to analyse data with the flexsurv package. I am not allowed to receive/analyse directly or on-site. I am however allowed to receive the analysis results.


            When we run the flexsurvreg() function on some data (here ovarian from the flexsurv package), the created object (here fitw) contains enough information to "re-create" or "back-engineer" the actual data. But then I would technically have access to the data I am not allowed to have.



            Answered 2022-Mar-29 at 11:01

            Setting, e.g. fitw$data <- NULL will remove all the individual-level data from the fitted model object. Some of the output functions may not work with objects stripped of data however. In the current development version on github, printing the model object should work. Also summary and predict methods should work, as long as covariate values are supplied in newdata - omitting them won't work, since the default is to take the covariate values from the observed data.



            Client-side render some components when using Angular Universal
            Asked 2022-Mar-14 at 23:59

            I am using Angular Universal for most of my website so that I can pre-render the content for SEO. It is meant to be a public facing site.

            I would like to be able to make certain components client-side rendered ONLY to avoid bundling content such as email addresses and social media links from being discoverable by web crawlers.

            I used the Angular Universal generated application to create my app. Currently, ALL my components are being rendered server-side. I couldn't find any specific clear example where someone used Angular in an elegant manner to achieve this specific goal. My intent is make my contact info and social media links components completely client-side rendered and added to the DOM at runtime to avoid bots and web crawlers from seeing it.

            How do others achieve this without doing something hacky?



            Answered 2022-Mar-14 at 23:59

            You can use isPlatformBrowser helper method for Angular and wrap all code in this helper method like below:



            How can I strip an open source app of trackers?
            Asked 2022-Mar-10 at 09:57

            I want to use an open source app (, but the amount od tracking the app does is something else. The GPL (under which the app is licenced) allows me to change this. Is there a tool or a guide to do just that?



            Answered 2022-Mar-10 at 08:43

            First you need a bit of development experience, Java and Gradle in this case.

            Then I would load the app project into Android Studio and compile it. You have to have no compile errors.

            Then check the build.gradle files for ad libraries (e.g. entries starting with implementation or androidImplementation). Comment them one-by-one out and after commenting out one check for compile errors where code from this libraries is used.

            Comment out also the code with errors because the library is no longer present. Repeat until all errors are fixed.



            How to hide ios app IP address from third party servers?
            Asked 2022-Feb-09 at 10:35

            My ios app retrieves some data from third party servers during runetime. For privacy reasons, I want to hide the IP addresses of my users, in order to prevent those servers to know them. How can I do that ?

            My idea is to set up a kind of "proxy server" or "VPN server" inbetween the app and the third party servers. Is that a good idea ?

            Thanks for your help!



            Answered 2022-Feb-09 at 10:35

            Yes, proxying is the right way to do this. You could do it with a VPN, but that's overkill, and requires considerable setup on the client side which you don't control.

            You can get a web server such as Nginx or Apache to act as a proxy directly through config options, or you can do it via scripting with PHP or whatever. I do the latter to provide a proxied service to The principle is quite straightforward:

            1. Accept a request from your client.
            2. On your server side, make a request (using an HTTP library, such as Guzzle) to the 3rd party service to get whatever is needed.
            3. Parse the response from there and create a response suitable for your client.

            This way the 3rd party service will only ever see the IP of your server, not your client, and you can choose exactly what data from the client you pass through. In my gravatar example, it sends an MD5 hash of the user's email address, which has its own privacy implications, but that's a separate problem!



            Unable to access HealthKit permissions for my app in Settings
            Asked 2022-Feb-08 at 20:19

            I recently published an app to the App Store that reads several metrics from HealthKit. It all seems to be working correctly in the app (permissions screen loads fine and data is being read okay) but when I go to Settings > Privacy > Health > MY APP, there is just a blank screen with no data.

            I've seen this bug with other apps (MyFitnessPal, Balance) and a forum recommended deleting and reinstalling, which fixed it for them. But it doesn't fix my app at all.

            Can anyone think of something I might have missed that's creating this issue?




            Answered 2022-Feb-08 at 20:19

            This should be addressed in iOS 15.4 Beta 1. Can you try there?

            You should also be able to access your apps authorizations from within Health App. Go to Profile View and navigate to your app under the Apps view.



            What parts of a URL are protected by TLS?
            Asked 2022-Jan-20 at 10:26

            If I go to , what parts of that URL are leaked to the man-in-the-middle ISP? I know they'll see "tumblr", but will they also see

            • cats
            • archive
            • x
            • y

            Also, if, for example, uses an image stored on a separate URL ( ), how much of that separate URL is exposed to the man in the middle? Just the kym-cdn? Or none of it, or what?

            Lastly, is any significant information about the length of the URL exposed to the man-in-the-middle ISP?



            Answered 2022-Jan-20 at 10:26

            Edited 1/20/2022, thank to Synchro in the comments

            • The domain name
            • and the hostname. Which can be found on unix systems inside /etc/hostname

            The domain name is leaked to the ISP via DNS request, and the hostname is leaked via SNI. Everything else is encrypted over TLS including the request URL and accompanied query parameters. However, if you use DNS over HTTPS (DoH) or DNS over TLS (DoT), these problems can be mitigated. ECH is a recent addition to the TLS 1.3 spec that fixes the hostname problem and has been automatically enabled alongside Firefox DoH since 2018.

            If you're curious what you're currently leaking, you can check out Firefox's website data integrity tool here

            TLS 1.3 spec

            HTTP over TLS spec

            DNS spec

            DNS over HTTPS spec



            Privacy Policy for the app on Google Play that doesn't collect any user data
            Asked 2022-Jan-02 at 23:27

            I want to publish a simple app that doesn't collect any personal data. It is an offline game (noughts and crosses), that doesn't require any account, and stores only field state on the user's device. I don't have any server and don't transfer the data anywhere, so it's fully offline. But Google Play asks for a Privacy Policy. How can I get it? Is there any standard Privacy Policy for such type of application? Can I just write something like "We don't store and transfer any personal data"?



            Answered 2022-Jan-02 at 23:27

            Easiest way is just to use a privacy policy generator such as this one:

            It's simple to use and only takes a few minutes to get you a privacy policy



            App Tracking Transparency popup do not appear
            Asked 2021-Dec-22 at 10:53

            I am trying to implement App Tracking Transparency in my app. I tried to do this on first ViewControllor, but it crashes the app after uploading to the test flight.

            After this, I found a lot of info that this should be done in appDelegate I did this way. Of course, I have set NSUserTrackingUsageDescription in Info.plist

            I tried to figure it out with this post.

            In the debugger, I always see "Not Determined". Could anyone please help with this?



            Answered 2021-Dec-22 at 10:53

            I found how to do this.

            Add in your first ViewController



            Client level differential privacy in Tensorflow Federated (Local DP)
            Asked 2021-Dec-20 at 09:45

            I want to implement local DP model using TFF, that is, each client trains it's own differentially private model and sends noisy gradients to the server, and the server just aggregates and distributes in a standard FL fashion. I tried changing the client optimizer to keras DP optimizer, but that didnt work. Any suggestions are appreciated.



            Answered 2021-Dec-20 at 09:45

            First, perhaps have a look at Differential Privacy in TFF tutorial which shows how to do central DP training in TFF. Once you understand that, I can see two different ways to change it to provide some local DP guarantees.

            1. Look at how the tff.learning.dp_aggregator is implemented. Instead of the pre-packaged tff.aggregators.DifferentiallyPrivateFactory, instantiate it with a tfp.DPQuery object that implements the local DP mechanism you are interested in. Perhaps an implementation you need already exists somewhere.
            2. Implement a custom aggregator from scratch doing exactly what you need. See Implementing Custom Aggregations tutorial for a starting point.



            How to use privateRuntimeConfig .env inside nuxt.config.ts for the Cloudinary module?
            Asked 2021-Nov-05 at 10:31

            This might be a really dumb question, but while trying to setup Cloudinary on my Nuxt application, I couldn't figure out how to utilise privateRuntimeConfig to pass the necessary keys to the Cloudinary plugin, because all of it is happening inside nuxt.config.ts.

            Meaning, that at the same place where I use privateRuntimeConfig, inside nuxt.config.ts, is also where I need to pass them to the Cloudinary plugin configuration, but this won't work. So how do I go about this?



            Answered 2021-Nov-04 at 18:54

            When using variables for modules like here (directly into nuxt.config.js/ts), you cannot reference the runtime variables.

            Simply use it directly like process.env.CLOUDINARY_CLOUD_NAME.
            More info on my complete answer here:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install machine-learning-diff-private-federated-learning

            Install Tensorflow 1.4.1 2 Download the files as a ZIP archive, or you can clone the repository to your local hard drive.
            Install Tensorflow 1.4.1 2 Download the files as a ZIP archive, or you can clone the repository to your local hard drive.
            Change to the directory of the download, If using macOS, simply run: bash This will download the MNIST data-sets, create clients and getting started.


            This project is provided "as-is" and any bug reports are not guaranteed to be fixed.
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            gh repo clone SAP-samples/machine-learning-diff-private-federated-learning

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