# TimeCrypt: Encrypted Data Stream Processing at Scale with Cryptographic Access Control

arXiv: https://arxiv.org/abs/1811.03457 · HTML: https://mlsanity.com/paper/1811.03457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [RoFL: Robustness of Secure Federated Learning](https://mlsanity.com/paper/2107.03311/) (2021)
- [Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs](https://mlsanity.com/paper/2509.18886/) (2025)
- [PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series](https://mlsanity.com/paper/2301.01838/) (2023)
- [Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users](https://mlsanity.com/paper/2502.08989/) (2025)

## Abstract (arXiv)

A growing number of devices and services collect detailed time series data that is stored in the cloud. Protecting the confidentiality of this vast and continuously generated data is an acute need for many applications in this space. At the same time, we must preserve the utility of this data by enabling authorized services to securely and selectively access and run analytics. This paper presents TimeCrypt, a system that provides scalable and real-time analytics over large volumes of encrypted time series data. TimeCrypt allows users to define expressive data access and privacy policies and enforces it cryptographically via encryption. In TimeCrypt, data is encrypted end-to-end, and authorized parties can only decrypt and verify queries within their authorized access scope. Our evaluation of TimeCrypt shows that its memory overhead and performance are competitive and close to operating on data in the clear.