In online data-processing services, a user typically hands over personal data to a remote server beyond the user's control. In such environments, the user cannot be assured that the data is protected from potential leaks. We introduce Pave, a new framework to guarantee data privacy while being processed remotely. Pave provides an arbitrary data-processing program with a sandboxed execution environment. The runtime monitor, PaveBox, intercepts all data flows into and out of the sandbox, allowing them only if they do not compromise user data. At the same time, it guarantees that the benign flows will not be hampered to preserve the program's functionality. As the PaveBox is built on top of Intel SGX, a user can verify the integrity and confidentiality of the PaveBox by remote attestation. We provide a formal model of Pave and prove its security and carry out the quantitative analysis with prototype-based experiments.

Simultaneous Translation (ST) involves translating with only partial source inputs instead of the entire source inputs, a process that can potentially result in translation quality degradation.Previous approaches to balancing translation quality and latency have demonstrated that it is more efficient and effective to leverage an offline model with a reasonable policy.However, using an offline model also leads to a distribution shift since it is not trained with partial source inputs, and it can be improved by training an additional module that informs us when to translate.In this paper, we propose an Information Quantifier (IQ) that models source and target information to determine whether the offline model has sufficient information for translation, trained with oracle action sequences generated from the offline model.IQ, by quantifying information, helps in formulating a suitable policy for Simultaneous Translation that better generalizes and also allows us to control the trade-off between quality and latency naturally.Experiments on various language pairs show that our proposed model outperforms baselines.1

Min-Kyung Park, Byung-Jun Lee. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Over the years, the Internet has become a field in which a small number of large Internet companies dominate most of the Internet services. As users get used to using their services, the users’ generated content and the data about their online behaviors are concentrated in such companies. This phenomenon, called “data consolidation”, has become a serious problem, which makes the Internet society seek to decentralize the current Internet. The decentralized Internet aims to (i) prevent the concentration of user data in a few giant companies like Google and Facebook, and (ii) give users full ownership and control of their data. Various technical solutions that address the data consolidation problem have been proposed; however, those solutions focus on somewhat different scopes of the problem often from their limited viewpoints. The main contributions in this paper are the following. First, we survey the solutions relevant to Internet decentralization based on the following criteria: data consolidation, data ownership, and the privacy of user data. Second, we suggest a holistic reference framework from a functional viewpoint, while the prior proposals in the literature handle a limited set of requirements. Last, we seek to identify remaining research issues, considering additional requirements that have not been addressed in the existing solutions.