# A Watermark-Based In-Situ Access Control Model for Image Big Data
## 1 Introduction  
The development of deep learning enables the analysis of a massive amount of image data. During these processes, how to analyse the image data while protecting images from leakage and exposure is a big challenge. Traditional access control policies may be invalid when images are stored again in different servers. For example, 4G and incoming 5G techniques enable smart phone users to share their images easily. When an image is uploaded to a service provider (e.g., Facebook), a user can set access privileges to control the access rights for the image so that the image can only be accessed by \"friends\" or the public. However, when the accumulated images are redistributed to other parties for further analysis (e.g., the Facebook-Cambridge Analytica scandal [1]), the access control policies which were stored in its original servers are lost. Thus, desired protection solutions should integrate access control policies with the image itself. In other words, even if the image is redistributed, the access control polices will be attached (in-situ) as well. In addition, a simple \"yes\" or \"no\" access control on an image does not work well. For example, when taking a photo with a smart phone, additional information such as location data, latitude/longitude, map, date and time, etc. are also included. Various privilegesshould be attached for that information. Consider geographic or remote sensing images as another example. A geographic image may consist of several areas/layers. Thus, differentiating access control for various areas/layers requires fine-grained and flexible access control policies. Recently, centrally regulated access control models (e.g., [2; 3]) have been intensively studied. However, they are not suitable for image data sharing and redistribution for the following reasons: Distributed data can be accessed with two modes: \"Yes\" for all or \"No\" for all. For data that cannot be accessed publically, the data cannot be distributed. Once data is distributed, it can be accessed by all accessors. Besides, for those data that must be in access control (classified data), control policies are difficult to define and change, especially when the data volume is large. For example, for different areas in a single image with different access policies, we must set up different regulations in central control servers. Moreover, classified data can be accessed only when remote policy conformance servers are available. The accessibility of the data relies on the availability of networks and the workload of central control servers. It constrains the convenience of remotely accessing data. Furthermore, access control regulation for a large volume of data results in a large delay. Each time accessors request images, they must first fetch access control policies on servers. In big data scenarios, accessing a response on servers results in a large burden and access delay. Finally, once data is distributed, the control domain is changed. Thus, the old management authority may not be available to control the data. Therefore, with the development of big data sharing and redistribution, traditional access control models based on central conformance should be improved to cater to the new requirements. In this paper, we design a novel access control model in which access control is conducted by specific clients and access policies are carried together with access objects themselves. Our proposed access control model has the following advantages: Access control policies are attached with image data. Regardless of how many times the data are further redistributed, access control policies are still incorporated with the data. Additionally, access control is fine-grained. For images with large size (e.g., geographic or remote sensing images), control strategies must be specific to different partial areas instead of the entire image. In other words, different parts in one image must conform to different access privileges.