引导式测验 · 硬 等级
人工智能与隐私测验
测试数据流、差异隐私、成员推理、间接重新识别、删除和隐私风险控制。
问题 1 的 8
哪种说法最能区分安全和隐私?
预览每个问题8 问题
当您参加测验时,就会出现答案和解释。
- 哪种说法最能区分安全和隐私?
- Security and privacy are identical concepts
- Encryption alone establishes consent
- Secure storage can still be a privacy problem if data are excessive or used unexpectedly
- Privacy only concerns passwords
- 在差分隐私分析中,epsilon 有何影响?
- The number of records in the input
- The privacy-accuracy tradeoff, with smaller epsilon generally giving stronger privacy
- The model’s training temperature
- The encryption key length
- 隶属推断攻击者询问一个人的记录是否在训练中。正在测试什么?
- Whether a user can reset a password
- Whether a record is duplicated in a database
- Whether a prompt contains a name
- Whether the model output leaks information about training-set membership
- 为什么删除名称无法使数据集匿名?
- Other attributes or combinations can re-identify people
- A name is the only possible identifier
- Data cannot be linked across tables
- Removing names always satisfies every privacy goal
- 哪种数据实践最符合指南的隐私原则?
- Keep every prompt indefinitely for any future use
- Collect only information needed for the stated task and set a retention period
- Copy all source records into each model call
- Treat local processing as proof of no privacy risk
- Why should a team inspect embeddings and indexes after deleting an uploaded document?
- Deleting a document automatically removes every copy everywhere
- Embeddings contain no data relationships
- Derived artifacts may still reveal information from the original data
- An index is only a user interface
- What should a team document about AI data processing?
- Only the model’s public name
- The number of support tickets
- A generic privacy slogan
- Which providers and internal services handle each data category
- Why does an assistant’s unsupported claim that data were deleted not suffice?
- The team should test deletion and account isolation through the actual application
- A chatbot response is a deletion receipt
- Data deletion is never relevant
- The assistant can inspect all vendor systems