La Confidential Hindi
Rene Seiler
FRA sponsors a voluntary confidential program allowing railroads and their employees to report close calls. C3RS provides a safe environment for employees to report unsafe events and conditions and employees receive protection from discipline and FRA enforcement. In addition, railroads receive protection from FRA enforcement for events reported within C3RS.
The Summit will bring together a community of innovators, organizational executives, regulators, business leaders, managers, security experts, data scientists, data analysts, AI/machine learning practitioners, data privacy experts, and researchers. Our mission is to educate, expose, and accelerate organizational initiatives around confidential computing, confidential AI, privacy-preserving generative AI, and LLMs. Customers and users from a wide range of industries who work with confidential data, including organizations in financial services, insurance, healthcare, manufacturing, AdTech, web3, and more, can see new innovations, hear user success stories, and learn best practices in confidential computing and privacy-preserving generative AI.
Meet and interact with the confidential computing and privacy-focused generative AI community and organizations evaluating and using confidential computing solutions. Network with attendees from a number of industries who work with confidential data, such as financial services, insurance, healthcare, manufacturing, AdTech, and more.
A governmental body is required to withhold certain types of information by statute. If information is confidential by statute, a governmental body generally cannot release the requested information. Here is a list of common types of information that is confidential by law.
A governmental body has the option to withhold non-confidential information in certain circumstances. In other words, a governmental body is not required to withhold requested information, but it may use its discretion to withhold the information. Here is a list of common types of information a governmental body may choose to withhold.
In either circumstance, a governmental is generally required to seek a ruling from OAG unless there is a previous determination allowing the governmental to withhold the type of information it seeks to withhold. Further, if a governmental body has previously released information voluntarily that is not confidential by law, the governmental body cannot claim a discretionary exception to withhold the previously released information. Review our Public Information Act Handbook (PDF) for more information on exceptions to disclosure.
Fine tuning for ChatGPT is not possible. Neither is it possible to fine tune next best text-davinci-003 (currently). Best option you have is the base Davinci model - and good luck fine tuning that and not going totes insane. Yikes.
One way to keep the information confidential (to some degree) is to store the data locally. By using Embedding, you will only send up the small pieces of information required to answer a specific question.
If the confidentially is related to knowledge or IP, you have to weigh up if small snippets taken out of context will cause you issues - or if they need to be in the larger surrounding text to make sense. If this is not a problem, embedding is also a good solution (for the reason described above)
I wonder if anyone could provide me with some advice, I am wanting to know if it is possible for members within a project to create issues on the Kanban board that only a few others/ upper management can see.
I am wanting to allow the opportunity for issues to be logged that contain confidential information that only management can see, is this possible, or does anyone have any other advice on how I might go ahead in creating this.
The basic idea of that is you define a scheme that has one or more security levels that identify people. If you apply a level to an issue, then the issue is only visible to the people named in the security level (and you can set rules in there like "Alice, Bob, the admin role and the managers group")
The slight quirk in your spec might be setting the level. To set a level, the person creating/editing the issue has to be included in the level. If you need to be able to hide issues even from the creator/editor, then you'll need to look at automating the setting of a level the user is not in.
The Nevada Confidential Address Program (CAP) is a program that helps victims of domestic violence, sexual assault, human trafficking and/or stalking from being located by the perpetrator through public records. The program provides a fictitious address and confidential mail forwarding services to individuals and families across Nevada.
CAP was established during the 1997 Legislative session in Senate Bill 155 authored by Senator Mark James. The program accepted its first participant in 1998. Nevada was the second state in the nation to provide this tool to the victims of domestic violence and sexual assault. The program was modeled after the State of Washington's Address Confidentiality Program that was established in 1991.
CAP was established to assist victims fleeing abusive situations and attempting a fresh start for themselves and their children. The program began when it became clear that in far too many cases, victims were being physically located through public records.
CAP participants are granted the use of a fictitious mailing address, which is maintained by the Division of Child and Family Services. When victims enter into business relationships with state and local agencies, the use of the fictitious address both maintains the victims' confidentiality and relieves those agencies of the difficult and costly responsibilities of maintaining confidential records. In this way, CAP participants are at a reduced risk for being tracked using state and local public records. The second part of the program provides for the protection of voter registration records. To be effective, CAP participation must be one part of a victim's long-term, personal security strategy.
Applicants do not enroll or apply directly with the Nevada Confidential Address Program. Any person interested in applying to the program must meet in person with a CAP Certified Advocate. Certified Advocates are located throughout the state in agencies and non-profit programs that provide counseling, referral, shelter, or assistance to victims of domestic violence, sexual assault, human trafficking and stalking.
Program staff reviews all completed applications. If an application is approved, program participants are enrolled for four years. All authorized participants are issued a Nevada Confidential Address Authorization Card and a new participant welcome packet explaining how to use the fictitious address effectively with government agencies.
When we launched Confidential Virtual Machines (VMs) in 2020, it was a pioneering solution that kept data encrypted while it was being processed. This helped ensure that your data was encrypted at rest, in transit, and in memory without requiring changes to your application or code, and is currently used by organizations including AstraZeneca, Bullish, HashiCorp, Matrixx Software, and Yellowdog. Confidential Space builds on that technology, and can empower organizations to collaborate with each other while maintaining confidentiality and control over their data.
Built on Confidential Computing, and leveraging remote attestation, Confidential Space runs workloads in a Trusted Execution Environment (TEE). Together with the hardened version of Container-Optimized OS (COS), data contributors can have control over how their data is used and which workloads are authorized to act on it. Finally, Confidential Space blocks the workload operator from influencing the workload in any way.
Financial institutions, such as banks and insurance agencies, need to collaborate to identify fraud or detect money laundering activity across their joint customer data set. Confidential Space can make this type of data sharing possible even though the data is highly sensitive, there are strict regulatory requirements, and these organizations often compete with each other. Financial institutions can be sure with Confidential Space that their data is only used for fraud detection while keeping business and confidential information private to the data owner.
Healthcare and medical technology companies can speed up development of pharmaceuticals and improve diagnostics using machine learning, without compromising patient data or risking non-compliance with international data privacy laws.
Web3 institutions can use Confidential Space to securely and instantly transact digital assets. Relying on multiparty computation (MPC), distributed collaborators can participate in an auditable signing process. Confidential Space's verifiable attestation can help ensure that all collaborators securely approve while never exposing their private signing keys to other parties, including the platform operator.
Confidential Space adds to our growing portfolio of products using Confidential Computing. Earlier this year, we launched to general availability Confidential Google Kubernetes Engine (GKE) Nodes and extended the flexibility of our Confidential VMs to new instance types. Additionally, Google Cloud Security and Google Project Zero partnered with the AMD firmware and product security teams on an in-depth security audit of the AMD technology that powers Confidential Computing, which you can read here.
By default, Google Cloud keeps all data encrypted, in-transit between customers and our data centers, and at rest. Confidential Computing can extend data privacy by protecting the confidentiality of your data and keeping it encrypted even while it is being processed.
With Confidential Space, we now enable new multi-party collaboration use cases, such as secure data sharing, privacy preserving analytics, and joint ML training. For more information, see our presentation at Next '22 with Brendan Taylor, CTO at MonetaGo, and sign up for the Preview here.
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