A Theory of Pricing Private Data

Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.Comment: 25 pages, 2 figures. Best Paper Award, to appear in the 16th International Conference on Database Theory (ICDT), 201

Transforming XML to RDF(S) with Temporal Information

The Resource Description Framework (RDF) is a model for representing resources on the Web. With the widespread acceptance of RDF in various applications (e.g., knowledge graph), a huge amount of RDF data is being proliferated. Therefore, transforming legacy data resources into RDF data is of increasing importance. In addition, time information widely exists in various real-world applications and temporal Web data has been represented and managed in the context of temporal XML. In this paper, we concentrate on transformation of temporal XML (eXtensible Markup Language) to temporal RDF data. We propose the mapping rules and mapping algorithms which can transform the temporal XML Schema and document into temporal RDF Schema and temporal RDF triples, respectively. We illustrate our mapping approach with an example and implement a prototype system. It is demonstrated that our mapping approach is valid

Thermally-Stable Passive Antenna Sensor for Strain and Crack Monitoring

An RFID patch antenna sensor can be used to wirelessly measure the strain and/or crack on a structural surface through the shift of its electromagnetic resonance frequency. According to electromagnetic theory, the resonance frequency �R of a patch antenna has an approximately linear relationship with strain �: [Formula] Previous design of antenna sensor with Rogers RT/duroid® 5880 substrate is sensitive to temperature change. A new RFID patch antenna sensor with thermally-stable substrate material Rogers RT/duroid® 6202 is designed and tested through both numerical simulation and laboratory experiments. A new RFID chip with much smaller footprint is adopted in the new design

Nondeterminstic ultrafast ground state cooling of a mechanical resonator

We present an ultrafast feasible scheme for ground state cooling of a mechanical resonator via repeated random time-interval measurements on an auxiliary flux qubit. We find that the ground state cooling can be achieved with \emph{several} such measurements. The cooling efficiency hardly depends on the time-intervals between any two consecutive measurements. The scheme is also robust against environmental noises.Comment: 4 pages, 3 figure

Adversarial Momentum-Contrastive Pre-Training for Robust Feature Extraction

Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which is not friendly in practical application. In this paper, we present a novel adversarial momentum-contrastive learning approach that leverages two memory banks to track the invariant features across different mini-batches. These memory banks can be efficiently incorporated into each iteration and help the network to learn more robust feature representations with smaller batches and far fewer epochs. Furthermore, after fine-tuning on the classification tasks, the proposed approach can meet or exceed the performance of some state-of-the-art supervised baselines on real world datasets. Our code is available at \url{https://github.com/MTandHJ/amoc}.Comment: 16 pages;6 figures; preprin