Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
Di Wang, Marco Gaboardi, Jinhui Xu
–Neural Information Processing Systems
In this paper, we revisit the Empirical Risk Minimization problem in the noninteractive local model of differential privacy. In the case of constant or low dimensions (pn), we first show that if the loss function is(,T)-smooth, wecanavoidadependence ofthesample complexity,toachieveerrorα,onthe exponential of the dimensionalityp with base1/α (i.e.,α p), which answers a questionin[19].
Neural Information Processing Systems
Feb-12-2026, 06:57:21 GMT
- Country:
- North America
- Canada > Quebec
- Montreal (0.04)
- United States
- Hawaii > Honolulu County
- Honolulu (0.04)
- Illinois > Cook County
- Chicago (0.04)
- New York > Erie County
- Buffalo (0.04)
- Hawaii > Honolulu County
- Canada > Quebec
- Oceania > Australia
- New South Wales > Sydney (0.04)
- North America
- Technology: