Efficient, noise-tolerant, and private learning via boosting

We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-tolerant PAC learners. To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension. We give two sample complexity bounds for our large-margin halfspace learner. One bound is based only on differential privacy, and uses this guarantee as an asset for ensuring generalization. This first bound illustrates a general methodology for obtaining PAC learners from privacy, which may be of independent interest. The second bound uses standard techniques from the theory of large-margin classification (the fat-shattering dimension) to match the best known sample complexity for differentially private learning of large-margin halfspaces, while additionally tolerating random label noise.https://arxiv.org/pdf/2002.01100.pd

Data Privacy Beyond Differential Privacy

Computing technologies today have made it much easier to gather personal data, ranging from GPS locations to medical records, from online behavior to social exchanges. As algorithms are constantly analyzing such detailed personal information for a wide range of computations, data privacy emerges as a paramount concern. As a strong, meaningful and rigorous notion of privacy, Differential Privacy has provided a powerful framework for designing data analysis algorithms with provable privacy guarantees. Over the past decade, there has been tremendous progress in the theory and algorithms for differential privacy, most of which consider the setting of centralized computation where a single, static database is subject to many data analyses. However, this standard framework does not capture many complex issues in modern computation. For example, the data might be distributed across self-interested agents, who may have incentive to misreport their data; and different individuals in the computation may have different expectations to privacy. The goal of this dissertation is to bring the rich theory of differential privacy to several computational problems in practice. We start by studying the problem of private counting query release for high-dimensional data, for which there are well-known computational hardness results. Despite the worst-case intractability barrier, we provide a solution with practical empirical performances by leveraging powerful optimization heuristics. Then we tackle problems within different social and economic settings, where the standard notion of differential privacy is not applicable. To that end, we use the perspective of differential privacy to design algorithms with meaningful privacy guarantees. (1) We provide privacy-preserving algorithms for solving a family of economic optimization problems under a strong relaxation of the standard definition of differential privacy---joint differential privacy. (2) We also show that (joint) differential privacy can serve as a novel tool for mechanism design when solving these optimization problems: Under our private mechanisms, the agents are incentivized to behave truthfully. (3) Finally, we consider the problem of using social network metadata to guide a search for some class of targeted individuals (for whom we cannot provide any meaningful privacy guarantees). We give a new variant of differential privacy---protected differential privacy---that guarantees differential privacy only for a subgroup of protected individuals. Under this privacy notion, we provide a family of algorithms for searching targeted individuals in the network while ensuring the privacy for the protected (un-targeted) ones

Maritime expressions:a corpus based exploration of maritime metaphors

This study uses a purpose-built corpus to explore the linguistic legacy of Britain’s maritime history found in the form of hundreds of specialised ‘Maritime Expressions’ (MEs), such as TAKEN ABACK, ANCHOR and ALOOF, that permeate modern English. Selecting just those expressions commencing with ’A’, it analyses 61 MEs in detail and describes the processes by which these technical expressions, from a highly specialised occupational discourse community, have made their way into modern English. The Maritime Text Corpus (MTC) comprises 8.8 million words, encompassing a range of text types and registers, selected to provide a cross-section of ‘maritime’ writing. It is analysed using WordSmith analytical software (Scott, 2010), with the 100 million-word British National Corpus (BNC) as a reference corpus. Using the MTC, a list of keywords of specific salience within the maritime discourse has been compiled and, using frequency data, concordances and collocations, these MEs are described in detail and their use and form in the MTC and the BNC is compared. The study examines the transformation from ME to figurative use in the general discourse, in terms of form and metaphoricity. MEs are classified according to their metaphorical strength and their transference from maritime usage into new registers and domains such as those of business, politics, sports and reportage etc. A revised model of metaphoricity is developed and a new category of figurative expression, the ‘resonator’, is proposed. Additionally, developing the work of Lakov and Johnson, Kovesces and others on Conceptual Metaphor Theory (CMT), a number of Maritime Conceptual Metaphors are identified and their cultural significance is discussed