Restricting details in privacy-related research to prevent abuse I'm starting to do research in machine learning on sensitive datasets, which sometimes seems like a double-edged sword. For instance, some projects involve showing how a company/organization/government can infer sensitive information about you and abuse it. I feel that this has to be published as it may already be abused somewhere or will be soon. At the same time, if I publish too many details then some corporations etc. may find it easy to implement and start abusing.

Do some conferences/journals accept papers that have scarce details on some parts of the modeling such that it may hinder abuse? It should be noted that while the code may be sent to the reviewers for a check, the data is sometimes of a sensitive nature such that it cannot be handed over.
From a practical perspective, even if you omit the implementation and mathematical details from your paper, large companies like Google and Facebook, or state agencies have significant amounts of manpower and talent. And if they really wanted to they could probably get very close to the results that you are obtaining in a short period of time. 

So it is unlikely that any obscuring on your part would halt these entities from following through with whatever agenda they have. On the contrary, omitting too many details will only make it harder for you to publish.

On a more philosophical note, any machine learning model can be used for good or for evil. Linear regression models I'm sure have helped people to win wars as well as eradicate diseases. As a result, one can easily argue that any attempt to prevent abuse is equally preventing progress in other domains.