… differential privacy, the personalized privacy model we introduce allows privacy requirements
to be specified at the user-level. … to take these individual privacy requirements into account …

… 23], we directly test for the privacy loss of the algorithm A. … Roughly speaking, this notion
measures the privacy loss when we run an (ε, δ)-item-level DP algorithm on two x, x′ drawn …

… To preserve privacy of users, (central) differential privacy requires an algorithm A has “similar…
differential privacy and privacy amplification by shuffling, which we will use in later sections. …

… think of differential privacy. In this work, we seek to explore users’ privacy expectations related
to differential privacy. … (1) whether users care about the protections afforded by differential …

… the privacy of IoT datasets, traditional methods usually calibrate random noises on the data
values to achieve differential privacy (… In addition, we derive the expression of the utility level …

… embedding models with user-level differential privacy; Fig. 1 provides a highlevel overview.
… fine-tuning, and partial aggregation to achieve strong privacy-utility trade-offs. The key to the …

… • How can we guarantee differential privacy, even against an adversary that has … differential
privacy when the algorithm must continually produce outputs? We call this differential privacy …

… use cases. To address this, we propose element level differential privacy, which extends
differential privacy to … about any particular “element” a user has, allowing better utility and more …

… , and (2) user-level sampling (ULS) and per-user gradient clipping. We derive a novel
userlevel DP accountant that allows us to compute provably tight privacy guarantees for ELS. …

… all, while the value 1 is the lowest level of privacy possible in our framework (however it still
… ε-differential privacy). Thus, the privacy weights of a user directly reflect his privacy attitudes …