We interact with location-aware devices on a daily basis. Such devices range from GPS-enabled cell-phones and tablets, to navigation systems. Each device can report a multitude of location data to centralized servers. Such location information, commonly referred to as geospatial data, can have tremendous benefits if properly processed and analyzed. If shared, such geo-spatial data can have significant impact for research and other uses. Sharing such information, however, can have significant privacy implications. In this talk, we will focus on the problem of releasing static geo-spatial data in a private manner. In particular, we will explore methods of releasing a synopsis of two-dimensional datasets while satisfying differential privacy. The key challenge to anonymizing geospatial datasets while satisfying differential privacy is ensuring the utility of anonymized dataset. In particular, there are two types of error that influence the utility of anonymized datasets. The first is the anonymization noise--a direct byproduct of the differential privacy mechanism. The second is a result of the granularity of data release and the nature of the dataset itself. In this talk, we will explore methods of publishing two-dimensional datasets with utility in mind. We will analyze the current state-of-the-art methods and explore alternative grid-based approaches that best balance the two sources of error.