Javad Ghareh Chamani

• Hong Kong University of Science and Technology

We propose GraphOS, a system that allows a client that owns a graph database to outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious primitives and trusted hardware to achieve a very strong privacy and efficiency notion which we call oblivious graph processing : the server learns nothing besides the number of...

In federated learning, multiple parties collaborate in order to train a global model over their respective datasets. Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some partial information may still be leaked across parties if this is done non-judiciously. In this work, we study th...

While cloud computing is growing at a remarkable speed, privacy issues are far from being solved. One way to diminish privacy concerns is to store data on the cloud in encrypted form. However, encryption often hinders useful computation cloud services. A theoretical approach is to employ the so-called fully homomorphic encryption, yet the overhead...

We study the problem of dynamic symmetric searchable encryption. In that setting, it is crucial to minimize the information revealed to the server as a result of update operations (insertions and deletions). Two relevant privacy properties have been defined in that context: forward and backward privacy. The first makes it hard for the server to lin...