Public agencies spend considerable resources collecting information about passenger travel in household travel surveys. These data are valuable for the rich and detailed information they provide, which contribute to regional and statewide travel demand models. These data have utility beyond travel demand modeling in their application to transportation policy and travel behavior research. As the demands on these data increase, so have the quantity of information collected. Detailed geospatial referencing of the home, work and other travel destinations are common practice and permit the integration with other spatially archived data sources, such as land use characteristics, transportation system information, and other built environment, social and economic data. Other public agencies, private consultancies, non-profits and educational institutions may benefit from access to the original data with applications to areas such as public health, equity, transportation safety and urban planning. But wide distribution of these important and expensive data is limited by the requirement to protect the confidentiality of survey participants, who are guaranteed anonymity in exchange for participation. Given the constraint of anonymity, data are often aggregated to a geographic level such as census tracts or transportation analysis zones (TAZs) before being disseminated to the public, which limits the utility of this information. This is particularly true as the need for more spatially explicit information is needed for such areas as non-motorized planning, evaluation access to transit, local accessibility studies, health impact analysis and other interests in linking transportation outcomes to detailed spatial data. To address these concerns, this project aims to examine an approach to permit dissemination of these spatially explicit data to a wider range of public constituents, while at the same time protecting the identities of study participants. To this end, this project will use geographical perturbation methods to add noise to the original data to protect confidentiality while at the same time allowing the detailed geo-spatial referencing to be included in the disseminated data. To do this, this research: (i) reviews geographical perturbation methods that seek to protect respondent confidentiality; (ii) outlines a framework for examining the disclosure risk in survey data; (iii) tests a procedure for implementing one promising perturbation practice, referred to as the donut masking technique, using data from a household activity travel survey in the Portland metropolitan region; and (iv) examines the disclosure risk and the error introduced to data derived from household location using this technique.