The first Workshop on Security and Privacy in GIS and LBS (SPRINGL 2008) was held on November 4 th at Irvine (CA) in conjunction with the ACM GIS Conference. The goal of the SPRINGL workshop series is to provide a forum for researchers working in the area of geospatial data security and privacy. Both security and privacy are critical for geospatial applications because of the dramatic increase and dissemination of geospatial data in several application contexts including homeland security, environmental crises, and natural and industrial disasters. Furthermore, geospatial infrastructures are being leveraged by companies to provide a large variety of location-based services (LBS) able to tailor services to users. However, despite the increase of publicly accessible geospatial information only little attention is being paid to how to secure geospatial information systems (GIS) and LBS. Privacy is also of increasing concern given the sensitivity of personally-identifiable location information. This is despite major advancements that have been made in secure computing infrastructures and the secure and privacy-preserving management of traditional (relational) data in particular. The Workshop spanned across security and privacy aspects, as they relate to the management of geospatial data and to the development of emerging LBS. Eight papers were selected for presentation and inclusion in the Workshop proceedings. In addition, the program included one invited talk by Gabriel Ghinita and an inaugural paper for SPRINGL by Elisa Bertino, Michael Gertz, Bhavani Thuraisingham, and Maria L. Damiani. Invited talk and inaugural paper. The invited talk by G. Ghinita focused on the trade-off between privacy and efficiency in privacy-preserving techniques in LBS. A taxonomy of privacy solutions was presented defined on the basis of the location transformation being used and the system architecture being adopted. The classification identifies three categories of techniques: (a) two-tier spatial transformations, (b) three-tier spatial transformations and (c) cryptographic transformations. Cryptographic transformations based on Private Information Retrieval offer the strongest privacy guarantees but may incur very significant processing overhead likely exceeding that of spatial transformation methods. The paper by Ghinita also identified several open research issues. The SPRINGL inaugural paper was presented by E. Bertino. The paper offers a comprehensive overview of the security challenges in geospatial data management and outlines a framework to deal with those issues. That framework encompasses a broad range of functions supporting: the specification of the policies and reasoning techniques for fine-grained access control to geospatial objects at varying resolution; interoperability of security policies for geospatial data; trust management, authentication, and secure third-party publication of geospatial data.