Confidentiality involves a set of rules or a promise usually executed through confidentiality agreements that limits the access or places restrictions on certain types of information.
Privacy law
By law, lawyers are often required to keep confidential anything pertaining to the representation of a client. The duty of confidentiality is much broader than the attorney–client evidentiary privilege, which only covers communications between the attorney and the client.
Both the privilege and the duty serve the purpose of encouraging clients to speak frankly about their cases. This way, lawyers can carry out their duty to provide clients with zealous representation. Otherwise, the opposing side may be able to surprise the lawyer in court with something he did not know about his client, which may weaken the client's position. Also, a distrustful client might hide a relevant fact he thinks is incriminating, but that a skilled lawyer could turn to the client's advantage (for example, by raising affirmative defenses like self-defense) However, most jurisdictions have exceptions for situations where the lawyer has reason to believe that the client may kill or seriously injure someone, may cause substantial injury to the financial interest or property of another, or is using (or seeking to use) the lawyer's services to perpetrate a crime or fraud. In such situations the lawyer has the discretion, but not the obligation, to disclose information designed to prevent the planned action. Most states have a version of this discretionary disclosure rule under Rules of Professional Conduct, Rule 1.6 (or its equivalent). A few jurisdictions have made this traditionally discretionary duty mandatory. For example, see the New Jersey and Virginia Rules of Professional Conduct, Rule 1.6.
In some jurisdictions, the lawyer must try to convince the client to conform his or her conduct to the boundaries of the law before disclosing any otherwise confidential information.
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Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Attorney–client privilege or lawyer–client privilege is the common law doctrine of legal professional privilege in the United States. Attorney–client privilege is "[a] client's right to refuse to disclose and to prevent any other person from disclosing confidential communications between the client and the attorney." The attorney–client privilege is one of the oldest privileges for confidential communications.
Information sensitivity is the control of access to information or knowledge that might result in loss of an advantage or level of security if disclosed to others. Loss, misuse, modification, or unauthorized access to sensitive information can adversely affect the privacy or welfare of an individual, trade secrets of a business or even the security and international relations of a nation depending on the level of sensitivity and nature of the information. This refers to information that is already a matter of public record or knowledge.
Classified information is material that a government body deems to be sensitive information that must be protected. Access is restricted by law or regulation to particular groups of people with the necessary security clearance and need to know, and mishandling of the material can incur criminal penalties. A formal security clearance is required to view or handle classified material. The clearance process requires a satisfactory background investigation.
In this seminar course students will get in depth understanding of mechanisms for private communication. This will be done by reading important papers that will be analyzed in the class. Students will
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...
EPFL2023
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A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...
Trusted execution environments enable the creation of confidential and attestable enclaves that exclude the platform and service providers from the trusted base. From its initial attestable state, a stateful enclave such as a confidential database can hold ...