A research group I'm participating in currently hosts all of their code in a private SVN repository. We'd like to open up our code and move most of it onto Github. The problem is, some of the code is sensitive and should not be opened up, but we still want it under version control. At the moment, we have the open code on Github and the private code still in the private SVN repository. Is there a good way to do this in a single Git repository?

With a single git repository, no. What you can do is use git submodules, which allow you to "combine" repositories. Keep your public code on github, create another, privately hosted, git repository for your private code which references the public code as a submodule. Changes made within the public submodule can be pushed up to github, and changes on github can be pulled back down, but changes outside the submodule won't be exposed to the public community. Although the code trees will be merged into a single root you will have to manage commits, pushes, and pulls independently between the separate modules, which many people find cumbersome and problematic, so you should do some experimentation with the workflow before distributing widely.

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Unless you want to write git hooks to encrypt/decrypt the sourcecode you'll have to live with two repos. When someone clones a git repo they literally make a clone of it, so it would not be possible to make parts of it private without encryption.

The idea is the submodule is a public git repository embedded inside a private git repository. You manage them separately, with the advantage of the private git repository having publict repository files embedded inside of the directory structure.

Yep, git submodules seem to solve the problem for us as well. We used to develop open source CMS & premium extensions (paid) in the same branch of our private repository. Now we decided to switch the core development to github public repo and split the development.

If project gets large, number of participants increases, confidentiality gets more critical one should really break the project into two and use the public as a shared backend as a library or plugin for your private.

I can see that we can train the base model with our private data using fine-tunings and embeddings (still trying to understand which one is the best option) but if we do train the model, do we get a private version of model like model ID or some sort of other information that can only be used by certain users and not accessed publicly?

In response to growing concerns among businesses over the potential for sensitive data leaks, Microsoft is planning to sell a private version of OpenAI's popular ChatGPT, according to a recent report from The Information. This offering will cater to companies hesitant to adopt ChatGPT, particularly those in heavily regulated industries like banking and healthcare, who worry about inadvertently sharing proprietary information with the chatbot.

Likely priced at several cents per token, Microsoft's private ChatGPT comes at a significant markup compared to the regular version's fraction of a cent per token. However, this pricing reflects the premium businesses are prepared to pay for increased security and data protection. The ongoing testing of the product by several financial institutions highlights a strong demand for a more secure ChatGPT offering. Microsoft salespeople have reportedly received inquiries from financial institutions and healthcare providers seeking a private version of the service.

The Information's report comes as OpenAI has hinted at a similar private ChatGPT offering aimed at businesses. Morgan Stanley has already signed on as an early customer. This move illustrates the potential market for privacy-focused versions of ChatGPT in industries that prioritize data security.

With the introduction of this private ChatGPT offering, Microsoft aims to differentiate its services from OpenAI's while still selling the startup's software. The existing relationships many large customers, including banks, have with Azure could provide Microsoft with an advantage in convincing them that their data will be handled securely and in compliance with local regulations.

As companies increasingly seek to leverage AI-powered tools for various tasks, the demand for privacy-focused solutions will continue to grow. Microsoft's private ChatGPT is a step towards addressing these concerns and ensuring data security for businesses across regulated industries.

The goal of this blog is to explain how to keep the private version of a planning model to which predictive forecasts were generated. Predictive forecasts are generated from historical data contained in the planning model, and are written back to a private version of this planning model. To retrieve those forecasts in a public version, the planner must publish the private version. A side effect of the publication is the deletion of the private version once it is copied to a public version. So, if planners want to revisit the predictive settings to get better forecasts, they have to restart from the beginning. There is a simple way to avoid this rework, as you can see from the figure below.

There is a new step before the publication of the private version. It involves the creation of a copy of the private version. It is this duplicated private version that will be published as a public version, and then deleted. The original private version is then still available to store new forecasts. So, the planner could go back to the first private version if needed.

Hi Thierry, upon publishing a private version to a public version, how does SAC decide which data range to override? And how can we influence that? For example: not to override any manually planned forecast data in public version.

When publishing a private version back to the public version it was copied from, the system only updates values that were actually changed in the private version. If nothing was changed in the private version then nothing will be updated in the public version. It is with this mechanism that predictive forecasts saved in the private version have been published to the public version which at the end contains historical data and predicted values for 2020. This is the default behavior.

Now there is a way to have more control. It is to enable Data Access Control for a dimension in the planning model. It restricts in the public version the data you can change by assigning read/write or only read permission. In your private version you can do any change you want, but changes on dimensions which have only read access in the public version will not be reported during the publication. You will find more details in the help about how to set Data Access Control.

Hi Community,

I have posted a similar question as a support request in the Marketplace Support system and received a very helpful answer by @syong.

It occurs that is it not possible to update a plugin from a public version to a private version (with access token). So in order to be able to estimate the experience for the user after the the update, you need to uninstall the app and re-install it in your dev system with the link provided in the Private Listings token overview. This process is not needed for the later update from the old public version to the new public version.

I hope this helps and I will continue to collect my experiences after the final release here.

SAP S/4HANA Cloud, private edition helps safeguard your existing SAP ERP investment while benefiting from greater flexibility. Tailor the software to meet your specific needs, retain company-specific configurations and customizations from your SAP ERP system, and access the latest capabilities that give your company a competitive edge.

Protect your enterprise with the built-in security features and add-on solutions from SAP.

SAP S/4HANA Cloud, private edition is our answer to enabling a cloud experience with the same functional scope and upgrade flexibility that SAP S/4HANA offers.

If a private key file is needed for a server to function correctly (say to communicate with client that has the public key we distribute), then, should this private key file be tracked in git or somewhere else?

Consider code audits.

An auditor needs to have access to source code to verify logic (and sometimes to a working program). They are not supposed to see (and hence be able to memorize) your private keys. I'm not going to go through whether it's realistic to memorize a 2048-bit key by glancing at it - sometimes reviews are done remotely.

A better way may be to put private key and public key somewhere with internet access. Each client can get public key directly without authorization. And the server will get the private key with authorization. It's just a bit thought, since I'm not the export for the domain.

In 2006, Andrea Griffini proposed a patch implementing a Cachedglobals+builtins lookup optimization. The patch adds a privatetimestamp field to the PyDictObject structure (dict type),the field has the C type size_t.

This second version of the PPP Reference Guide, as the first one, presents a global overview of the diversity of approaches and experiences in the implementation of public-private partnerships (PPPs), providing an entry point to the substantial body of knowledge on PPPs that has been built up by practitioners in governments, the private sector, international institutions, and academitc. With due care not to increase the overall size of the Guide, this version includes new references and examples.

The go command may be configured to contact proxies or source control serversusing the GOPROXY environment variable, which accepts a list of proxy URLs.The list may include the keywords direct or off (see Environmentvariables for details). List elements may be separatedby commas (,) or pipes (|), which determine error fallback behavior. When aURL is followed by a comma, the go command falls back to later sources onlyafter a 404 (Not Found) or 410 (Gone) response. When a URL is followed by apipe, the go command falls back to later sources after any error, includingnon-HTTP errors such as timeouts. This error handling behavior lets a proxy actas a gatekeeper for unknown modules. For example, a proxy could respond witherror 403 (Forbidden) for modules not on an approved list (see Private proxyserving private modules). be457b7860

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