A randomised mechanism M satisfies (ε, δ)-DP if for all datasets D, D' differing by one record, and all outputs S: Pr[M(D) ∈ S] ≤ e^ε · Pr[M(D') ∈ S] + δ
Parameter
Meaning
ε (epsilon)
Privacy budget — smaller = stronger privacy
δ (delta)
Failure probability — probability DP guarantee breaks
A paper uses the Laplace mechanism to release a differentially private count query.
What does the Laplace mechanism do?
Laplace mechanism for numeric queries:
M(x) = f(x) + Lap(sensitivity / ε)
Term
Meaning
f(x)
The true query result
Sensitivity
Max change in f when one record changes
ε
Privacy budget
Gaussian mechanism: uses Gaussian noise, satisfies (ε, δ)-DP. Key vocabulary: noise calibration, sensitivity of a function (L1/L2), noise scale, privacy-accuracy trade-off.
4 / 10
A product team discusses implementing local differential privacy (LDP) vs. global (central) differential privacy.
What is the key architectural difference?
Local vs. global DP architecture:
Model
Where noise is added
Trust assumption
Example
Local DP
On user's device
No trusted server needed
Apple iOS telemetry, Google RAPPOR
Global DP
At the curator/server
Requires trusted curator
US Census DP, TensorFlow Privacy
Trade-off: LDP has worse accuracy for the same ε because noise is applied before aggregation. Key vocabulary: randomised response, shuffler model, trusted curator, federated learning + DP.
5 / 10
An engineer says: "The function has a sensitivity of 1 for this query."
What does sensitivity of a function mean in differential privacy?
Global sensitivity = max change in output over all pairs of neighbouring datasets:
Query
L1 sensitivity
Why
COUNT(*)
1
Adding/removing one record changes count by at most 1
SUM(age) — age 0–100
100
One record can contribute up to 100
MEAN(age)
100/n
Changes by at most range/n
Higher sensitivity → more noise needed → less accuracy. Key vocabulary: L1 sensitivity (Manhattan), L2 sensitivity (Euclidean), local sensitivity, smooth sensitivity, clipping to bound sensitivity.
6 / 10
Reviewer: 'This query uses a Laplace mechanism with a scale of 1.0. Is that appropriate for the data distribution?'
Which statement best describes the reviewer's concern regarding the use of this scale?
The reviewer's concern revolves around the scale parameter within the Laplace mechanism. A larger scale weakens the noise added to the query result, potentially reducing the privacy budget (ε). Therefore, a scale of 1.0 is often preferred for stronger differential privacy guarantees, though the optimal value depends on the specific data and desired ε.
7 / 10
DevOps Lead (via Slack): 'Just ran the DP monitoring. We're seeing a high noise budget usage on the user-activity query – it's exceeding our target of 10%. Anyone have any ideas?'
What is the *primary* reason for this increased noise budget utilization?
The key here is understanding that differential privacy relies on adding calibrated noise. Higher sensitivity of the query (meaning a small change in the input can significantly affect the output) necessitates more noise to maintain the desired ε value and therefore, a higher noise budget usage.
8 / 10
PR Description: 'Implemented a local differential privacy (LDP) approach for calculating user retention. This reduces the risk of centralized data breaches while still providing valuable insights.'
Which statement best reflects the core benefit of using LDP in this scenario?
LDP's primary advantage lies in its decentralized nature. By processing data locally on each device (or user segment), it avoids centralizing sensitive information that could be exploited in a breach – this fundamentally reduces the risk compared to global DP where all data is aggregated centrally.
9 / 10
Engineer (during standup): 'I've been experimenting with adding a multiplicative accumulator to our query results. It seems like it might help us achieve better privacy at the same level of accuracy.'
What does the engineer likely mean by 'multiplicative accumulator' in the context of differential privacy?
The 'multiplicative accumulator' refers to a technique where noise is added multiplicatively to each query result. This can be more efficient than additive methods (like the Laplace mechanism) for certain types of queries and allows for controlling the overall noise budget more precisely – effectively scaling the privacy protection.
10 / 10
API Response (from a data analysis service): `{'query_result': 12345, 'noise_added': 5.2, 'epsilon': 0.8}`
What does the 'noise_added' value represent in this API response?
The 'noise_added' value is a direct consequence of applying differential privacy. It quantifies the calibrated randomness injected into the query result to ensure that the output remains statistically similar regardless of any individual user's data being included in the analysis – this is how differential privacy guarantees are achieved.
What will I learn from the "Differential Privacy Vocabulary Exercises" exercise?
Practice English vocabulary for differential privacy: epsilon, delta, sensitivity, Laplace mechanism, Gaussian mechanism, local vs. global differential privacy.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to use with no account, sign-up, or paywall required.
How many questions are in this exercise?
This set contains 10 multiple-choice questions, each with a detailed explanation shown after you answer.
Do I need to create an account to track my progress?
No account is required. Your progress bar and score reset each time you reload the page, but you can retry the exercise as many times as you like.
Who is this Synthetic Data Vocabulary exercise for?
This exercise is built for IT professionals and non-native English speakers who need to read, write, and discuss synthetic data vocabulary topics confidently at work.
What happens if I answer a question incorrectly?
You will see the correct answer highlighted along with a detailed explanation of why it is correct -- so every wrong answer becomes a learning moment, not just a lost point.
Can I retry this exercise?
Yes -- click "Try again" on the results screen at any time to reset your score and go through all the questions again.
How long does this exercise take to complete?
Most learners finish all 10 questions in under 10 minutes, since each question is answered by clicking a single option.
Where can I find more Synthetic Data Vocabulary exercises?
See the full Synthetic Data Vocabulary exercises hub for more vocabulary drills on this topic.
Is this exercise mobile-friendly?
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