Many application domains, such as real-time financial analysis, e-healthcare systems, sensor networks, are characterized by continuous data streaming from multiple sources and through intermediate processing by multiple aggregators. Keeping track of data provenance in such highly dynamic context is an important requirement, since data provenance is a key factor in assessing data trustworthiness which is crucial for many applications. Provenance management for streaming data requires addressing several challenges, including the assurance of high processing throughput, low bandwidth consumption, storage efficiency and secure transmission. In this talk, I will discuss a novel approach to securely transmit provenance for streaming data (focusing on sensor network) by embedding provenance into the inter-packet timing domain while addressing the above mentioned issues. As provenance is hidden in another host-medium, our solution can be conceptualized as watermarking technique. However, unlike traditional watermarking approaches, we embed provenance over the inter-packet delays rather than in the sensor data themselves, hence avoiding the problem of data degradation due to watermarking. Provenance is extracted by the data receiver utilizing an optimal threshold-based mechanism which minimizes the probability of provenance decoding errors. The resiliency of the scheme against outside and inside attackers is established through an extensive security analysis. Experiments show that our technique can recover provenance upto a certain level against perturbations to inter-packet timing characteristics.