Order-Revealing Encryption and the Hardness of Private Learning

An order-revealing encryption scheme gives a public procedure by which two ciphertexts can be compared to reveal the ordering of their underlying plaintexts. We show how to use order-revealing encryption to separate computationally efficient PAC learning from efficient $(\epsilon, \delta)$-differentially private PAC learning. That is, we construct a concept class that is efficiently PAC learnable, but for which every efficient learner fails to be differentially private. This answers a question of Kasiviswanathan et al. (FOCS '08, SIAM J. Comput. '11). To prove our result, we give a generic transformation from an order-revealing encryption scheme into one with strongly correct comparison, which enables the consistent comparison of ciphertexts that are not obtained as the valid encryption of any message. We believe this construction may be of independent interest.Comment: 28 page

Input Prioritization for Testing Neural Networks

Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data---the expected output, a.k.a. label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization---confidence, uncertainty, and surprise---and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%

Human adenovirus in municipal solid waste leachate and quantitative risk assessment of gastrointestinal illness to waste collectors

Leachate is a variable effluent from waste management systems generated during waste collection and on landfills. Twenty-two leachate samples from waste collection trucks and a landfill were collected from March to December 2019 in the municipality of Rio de Janeiro (Brazil) and were analyzed for Human Adenovirus (HAdV), bacterial indicators and physico-chemical parameters. For viral analysis, samples were concentrated by ultra centrifugation and processed for molecular analysis using QIAamp Fast DNA Stool mini kit (R) for DNA extraction followed by nested-PCR and qPCR/PMA-qPCR TaqMan (R) system. HAdV was detected by nested-PCR in 100% (9/ 9) and 83.33% (12/13) of the truck and landfill leachate samples, respectively. Viral concentrations ranged from 8.31 x 10(1) to 6.68 x 107 genomic copies per 100 ml by qPCR and PMA-qPCR. HAdV species A, B, C, and F were characterized using nucleotide sequencing. HAdV were isolated in A549 culture cells in 100% (9/9) and 46.2% (6/13) from truck and landfill leachate samples, respectively. Regardless of the detection methods, HAdV concentration was predicted by the quantity of total suspended solids. A quantitative microbial risk assessment was performed to measure the probability of gastrointestinal (GI) illness attributable to inadvertent oral ingestion of truck leachate, revealing the higher probability of disease for the direct splashing into the oral cavity (58%) than for the gloved hand-to-mouth (33%). In a scenario where waste collectors do not wear gloves as protective personal equipment, the risk increases to 67%. This is the first study revealing infectious HAdV in solid waste leachate and indicates a potential health risk for waste collectors