Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Near, Joseph A."'
Choreographic programming (CP) is a paradigm for implementing distributed systems that uses a single global program to define the actions and interactions of all participants. Library-level CP implementations, like HasChor, integrate well with mainst
Externí odkaz:
http://arxiv.org/abs/2412.02107
Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a three-phase method that initializes a model using data synthesis from a
Externí odkaz:
http://arxiv.org/abs/2410.17566
The miniKanren and Relational Programming Workshop is a workshop for the miniKanren family of relational (pure constraint logic programming) languages: miniKanren, microKanren, core.logic, OCanren, Guanxi, etc. The workshop solicits papers and talks
Externí odkaz:
http://arxiv.org/abs/2409.06505
Autor:
Skalka, Christian, Near, Joseph P.
Secure Multi-Party Computation (MPC) is an important enabling technology for data privacy in modern distributed applications. Currently, proof methods for low-level MPC protocols are primarily manual and thus tedious and error-prone, and are also non
Externí odkaz:
http://arxiv.org/abs/2407.16504
Choreographic programming is a concurrent paradigm in which a single global program called a choreography describes behavior across an entire distributed network of participants. Choreographies are easier to reason about than separate programs runnin
Externí odkaz:
http://arxiv.org/abs/2406.13716
Autor:
Bates, Mako, Near, Joseph P.
Concurrent distributed systems are notoriously difficult to construct and reason about. Choreographic programming is a recent paradigm that describes a distributed system in a single global program called a choreography. Choreographies simplify reaso
Externí odkaz:
http://arxiv.org/abs/2403.05417
Autor:
Bates, Mako, Near, Joseph P.
Formal methods for guaranteeing that a protocol satisfies a cryptographic security definition have advanced substantially, but such methods are still labor intensive and the need remains for an automated tool that can positively identify an insecure
Externí odkaz:
http://arxiv.org/abs/2403.04991
Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently developed DP tools aim to make DP implementation easier, but limited res
Externí odkaz:
http://arxiv.org/abs/2309.13506
Recent secure aggregation protocols enable privacy-preserving federated learning for high-dimensional models among thousands or even millions of participants. Due to the scale of these use cases, however, end-to-end empirical evaluation of these prot
Externí odkaz:
http://arxiv.org/abs/2302.10084
Autor:
Maughan, Krystal, Near, Joseph P.
Surveys are an important tool for many areas of social science research, but privacy concerns can complicate the collection and analysis of survey data. Differentially private analyses of survey data can address these concerns, but at the cost of acc
Externí odkaz:
http://arxiv.org/abs/2209.10908