To answer (1), if you seed the randomiser with a known static value—e.g. the co-ordinates, or a database record ID—then repeat queries will return the same mang

To answer (1), if you seed the randomiser with a known static value—e.g. the co-ordinates, or a database record ID—then repeat queries will return the same mangled result. Or an even simpler, more trivial solution would be to store mangled values next to the true values. Then all of your APIs and services can pick the appropriate value for the circumstance. To make it more obvious that the data is imprecise, I would also ensure that the mangled geo co-ordinates returned with a minimum number of decimal places. Three decimal places gives you a precision of 50–100 meters, depending on latitude. Four gives you 5-10 meters, which is the most you'd ever need for an approximate house location. The concern that comes to my mind is the size of the randomisation and whether they factor in the size of surrounding properties. The level of imprecision required in a dense city will have little to no effect on farmland properties. I would dynamically adjust the random amplitude so that the circle of confusion covered a minimum number of properties. At minimum you'd find a database that listed median property sizes for a given postal code/zip code.