Basic Idea |
Privacy loss accumulates over queries executed
sequentially. |
Multiple queries are executed simultaneously, and the
strongest privacy parameter is maintained. |
Privacy Bound |
Cumulative privacy loss is bounded by the sum
of ε values across queries. |
Privacy bound is determined by the maximum
ε value among the queries. |
Privacy Amplification |
Privacy loss increases linearly with
the number of queries. |
Privacy amplification depends on the query with
the highest ε. |
Trade-off |
Balances privacy loss with sequential execution
order. |
Prioritizes strongest privacy guarantee among parallel
queries. |
Example |
If each query has ε=0.1, after 10 queries, cumulative
ε=1. |
If queries have ε=0.1, 0.2, and 0.3, overall privacy is dominated
by ε=0.3. |
Applications |
Useful for cases where queries build on previous
results. |
Suitable when multiple queries are independent and can be
executed simultaneously. |
Complexity |
Cumulative privacy loss calculation becomes
complex for a large number of queries. |
Determining the maximum ε among
parallel queries is straightforward. |
Privacy Leakage |
Can lead to substantial privacy loss if many
queries are executed sequentially. |
Maintains stronger privacy when
queries are executed in parallel. |
Individual Impact |
Privacy loss per query may decrease if
queries are less sensitive. |
Sensitive queries can dominate overall
privacy guarantee. |
Dynamic Settings |
Well-suited for settings where queries
evolve or adapt based on previous results. |
Not designed to adapt to
dynamic query changes. |
Advanced Techniques |
Advanced composition theorems can provide
more accurate cumulative bounds. |
Similarly, advanced theorems can
offer tighter privacy bounds for parallel execution. |