Over the past few years, Machine Learning-as-a-Service (MLaaS) has received a
surging demand for supporting Machine Learning-driven services to offer
revolutionized user experience across diverse application areas. MLaaS provides
inference service with low inference latency to application users based on an
ML model trained using a dataset collected from numerous individual data
owners. Recently, for the sake of data owners' privacy and to comply with the
"right to be forgotten (RTBF)" as enacted by data protection legislation, many
machine unlearning methods have been proposed to remove data owners' data from
trained models upon their unlearning requests. However, despite their promising
efficiency, almost all existing machine unlearning methods handle unlearning
requests in a manner that is independent of inference requests, which
unfortunately introduces new security and privacy vulnerabilities for machine
unlearning in MLaaS. In this paper, we propose the ERASER framework for machinE
unleaRning in MLaAS via an inferencE seRving-aware approach. ERASER proposes a
novel certified inference consistency mechanism that reduces inference latency
by selectively postponing unlearning execution incurred by unlearning requests
from data owners, while strictly adhering to the RTBF principle. ERASER offers
three groups of design choices to allow for tailor-made variants that best suit
the specific environments and preferences of different MLaaS systems. Extensive
empirical evaluations across various settings confirm ERASER's effectiveness,
e.g., it can effectively save up to 99% of inference latency and 31% of
computation overhead over the inference-oblivion baseline.

提出了 ERASER 框架，通过一个推理服务意识的方法来实现 MLaaS 中的机器解学习。ERASER 提供了一种新颖的认证推理一致性机制，通过有选择性地延迟来自数据所有者的解学习请求，从而降低推理延迟，并严格遵守 RTBF 原则。在各种设置下进行了大量实证评估，确认了 ERASER 的效果，例如，它可以有效节省高达 99% 的推理延迟和 31% 的计算开销。