W.r.t. security, our current approach is to shard access rights and encryption on a database level and just provide many databases, one for each user. This is obviously not the most space efficient, but the most general approach. If users can share access keys and data we can also do structural sharing between these instances and factorize further. We envision doing joins potentially over dozens of distributed Datahike instances in a global address space during single queries. Since the indices are amortized data structures it does not make too much sense to encrypt chunks of them for different users as this defeats the optimality guarantees of B+-trees, i.e. you could have very bad scan behaviour over huge ranges of encrypted Datoms. How have you tried to partition the data? This is an interesting problem.
We can also expose the datahike-server query endpoint directly and you can write static checks for access right restrictions. We only do this so far to limit the usage of builtin functions to safe ones, but you could also go ahead and do the same for more complex access controls. Some work in this direction for Datahike has also been done here: https://github.com/theronic/eacl Doing this openly on the internet will also require a resource model to fend of denial of service attacks, fortunately Datalog engines can have powerful query planners and we can restrict our runtime to limited budgets as well.