With the proliferation of sensitive time series data being collected, stored, and queried in cloud environments, it is imperative to integrate owner-centric privacy controls with encrypted data processing to enable secure and flexible data sharing. Existing systems, however, often lack the flexibility to express nuanced privacy policies and fail to adequately protect sensitive metadata. Adversaries can exploit declared policies, query attributes, and data access patterns to infer confidential information about data owners. In this paper, we present Dobby, a privacy-preserving time series data analytics system that enforces fine-grained and flexible access policies through function secret sharing (FSS). Dobby ensures robust privacy protection for policies, query attributes, and access patterns, while achieving malicious security in a two-party computation setting. Furthermore, we propose an optimized algorithm to streamline policy evaluation, significantly reducing communication overhead. Our evaluation demonstrates the efficiency and practicality of Dobby. For a query involving 100 ciphertexts, the system achieves a query latency of approximately 3.3 s on 10,000 data streams, each governed by an access policy comprising eight conditions.