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Certified Robustness to Adversarial Examples with Differential Privacy
TLDR
This paper presents the first certified defense that both scales to large networks and datasets and applies broadly to arbitrary model types, based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism.
View on IEEE
Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence
TLDR
Sunlight's default functioning strikes a balance to provide the first system that can diagnose targeting at fine granularity, at scale, and with solid statistical justification of its results, as well as running two measurement studies of targeting on the web, both the largest of their kind.
View on ACM
XRay: Enhancing the Web's Transparency with Differential Correlation
TLDR
XRay is developed, the first fine-grained, robust, and scalable personal data tracking system for the Web, which achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.
View PDF on arXiv
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. The ability to distinguish data that is actually needed…
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Synapse: a microservices architecture for heterogeneous-database web applications
TLDR
This work presents Synapse, an easy-to-use, strong-semantic system for large-scale, data-driven Web service integration, developed on top of the popular Web framework Ruby-on-Rails, which supports data replication among a wide variety of SQL and NoSQL databases.
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Harvesting Randomness to Optimize Distributed Systems
TLDR
This work shows how to collect data from existing systems, without modifying them, to evaluate new policies, without deploying them, and has the potential to accurately estimate a policy's performance without the risk or cost of deploying it on live traffic.
View on ACM
On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning
TLDR
This work proposes PixelDP, a strategy for learning robust deep neural networks based on formal DP guarantees, and observes that the semantic of DP is closely aligned with the formal definition of robustness to adversarial examples.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform
TLDR
S Sage is presented, the first ML platform that enforces a global differential privacy guarantee across all models produced from a sensitive data stream and a novel iterative training process that trains a model on increasing amounts of data from a stream until, with high probability, the model meets developer-configured quality criteria.
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Improving the Transparency of the Sharing Economy
TLDR
The results provide the first complete view of the occupancy and the distribution of revenue, revealing important trends that generalize previous observations and finding that previous observations that seemed at odds are all explained by a variant of the "inspection paradox".
View on ACM
Enhancing Selectivity in Big Data
TLDR
This study shows that, for a wide and important class of workloads, only a fraction of the data is needed to approach state-of-the-art accuracy.
View on IEEE
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