Student Posters Repository

Nowadays, billions of people use a huge variety of online services on a daily basis. These services are successful because they provide personalized results depending on the user profile (user’s identity associated with their personal data).  Majority of online services relies on third party cloud services either for data storage or for data processing. A leakage of user profile, which contains sensitive information, may jeopardize the end user privacy.

The Data partitioning approach could be considered to overcome the issue. This approach consists of dividing a sensitive user profile into non-sensitive components. Then the code is partitioned based on data partitions and it is deployed either on multiple server or on multiple enclaves within a same server. However, this approach is challenging to implement, because the developer manually partitions the code and tracks the data flow. Even a small careless mistake may lead to unintended data leakage.

My PhD thesis has the goal of developing a domain specific language (DSL) to enforce privacy. The language consists of annotating C code to express data sensitivity. Furthermore,  the DSL ensures correctness using static analysis, automatically partitions the code and deploys it. We modify the LLVM compiler for the implementation. Our language reduces coding time and attack surface of the code, prevents unintended data leakage and ensures portability for different frameworks.