Enhancing Technologies Flashcards

Which privacy-enhancing technology allows multiple parties to jointly compute a function over their inputs without revealing those inputs to each other?

Answer: Secure multi-party computation (SMPC)

Secure multi-party computation (SMPC) enables multiple parties to collaboratively compute results while keeping each party's input private from the others.

A company wants to allow data analysts to query a database without ever seeing raw personal data. Which PET best fits this requirement?

Answer: Homomorphic encryption

Homomorphic encryption allows computations to be performed on encrypted data, so analysts can derive results without ever decrypting or viewing the raw personal data.

In the context of federated learning, what is the primary privacy benefit compared to centralized machine learning?

Answer: Raw training data stays on local devices rather than being sent to a central server

Federated learning trains models locally on devices and shares only model updates (not raw data) with a central coordinator, keeping personal data on the originating device.

A zero-knowledge proof (ZKP) is best described as a cryptographic method that allows a prover to:

Answer: Demonstrate knowledge of a secret without revealing the secret itself

A zero-knowledge proof lets one party (the prover) convince another (the verifier) that a statement is true without disclosing any information beyond the truth of that statement.

Which technique adds controlled statistical noise to query results so that individual records cannot be singled out, while still allowing useful aggregate analysis?

Answer: Differential privacy

Differential privacy adds carefully calibrated noise to outputs so that the presence or absence of any single individual's data cannot be detected from query results.

Synthetic data generation is considered a privacy-enhancing technology primarily because:

Answer: It produces statistically representative data with no direct link to real individuals

Synthetic data is artificially generated to mirror the statistical properties of real data without containing actual personal information, eliminating direct privacy risk.

Which of the following is a key limitation of k-anonymity as a privacy-enhancing technique?

Answer: It is vulnerable to homogeneity and background knowledge attacks

K-anonymity can be defeated by homogeneity attacks (when all records in a group share the same sensitive value) and background knowledge attacks using external information.