The Moral Character of Cryptographic Work

By Phillip Rogaway. 2015

183 citations recorded by the source corpus.

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Citations in the corpus

The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “…that only a small fraction of a company’s employees, with appropriate clearance and training, should be able to access the raw data of those who contribute their data to the trusted curator model, whereas the majority of employees should only see the privacy-preserving version of it [54].”
The Values Encoded in Machine Learning Research (2021, background): “This work is closely related to Rogaway [56], who notes that cryptography has political and moral dimensions and argues for a cryptography that better addresses societal needs.”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “Hence it may be more constructive to strive for an informed public, addressing the dual use danger with a preemptive disclosure (Rogaway, 2015; Hovy and Spruit, 2016) - letting potential abusers know that certain illegal and unethical purposes of using personalized models are not supported, and…”
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Unobservable Communication over Fully Untrusted Infrastructure Unobservable Communication over Fully Untrusted Infrastructure (unknown): “…interest motivated by an increase in service providers disclosing their users' information without consent [7] [13] [16] [83] [90] [109], as well as questionable mass surveillance practices [15] [27] [50] [62] [63] that defy existing privacy laws and long-held beliefs [44] [116] [128] [129] [133].”
Large Language Models Can Be Strong Differentially Private Learners (2021, background): “On the other hand, we acknowledge that successful private learning has the potential to motivate companies to more aggressively collect user data, which could result in long-term privacy harms (Rogaway, 2015; Solove, 2005).”
AI Art and its Impact on Artists (2023, background): “We thus advocate for a computer science education system that stresses the manner in which power interacts with technology [19, 105].”