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46 results on '"Nie, Yiwen"'

2. Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability

Author

Wang, Shaowei, Du, Jiachun, Yang, Wei, Diao, Xinrong, Liu, Zichun, Nie, Yiwen, Huang, Liusheng, and Xu, Hongli

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Voting plays a central role in bringing crowd wisdom to collective decision making, meanwhile data privacy has been a common ethical/legal issue in eliciting preferences from individuals. This work studies the problem of aggregating individual's voting data under the local differential privacy setting, where usefulness and soundness of the aggregated scores are of major concern. One naive approach to the problem is adding Laplace random noises, however, it makes aggregated scores extremely fragile to new types of strategic behaviors tailored to the local privacy setting: data amplification attack and view disguise attack. The data amplification attack means an attacker's manipulation power is amplified by the privacy-preserving procedure when contributing a fraud vote. The view disguise attack happens when an attacker could disguise malicious data as valid private views to manipulate the voting result. In this work, after theoretically quantifying the estimation error bound and the manipulating risk bound of the Laplace mechanism, we propose two mechanisms improving the usefulness and soundness simultaneously: the weighted sampling mechanism and the additive mechanism. The former one interprets the score vector as probabilistic data. Compared to the Laplace mechanism for Borda voting rule with $d$ candidates, it reduces the mean squared error bound by half and lowers the maximum magnitude risk bound from $+\infty$ to $O(\frac{d^3}{n\epsilon})$. The latter one randomly outputs a subset of candidates according to their total scores. Its mean squared error bound is optimized from $O(\frac{d^5}{n\epsilon^2})$ to $O(\frac{d^4}{n\epsilon^2})$, and its maximum magnitude risk bound is reduced to $O(\frac{d^2}{n\epsilon})$. Experimental results validate that our proposed approaches averagely reduce estimation error by $50\%$ and are more robust to adversarial attacks.

Published
2019

5. Mutual Information Optimally Local Private Discrete Distribution Estimation

Author

Wang, Shaowei, Huang, Liusheng, Wang, Pengzhan, Nie, Yiwen, Xu, Hongli, Yang, Wei, Li, Xiang-Yang, and Qiao, Chunming

Subjects
Computer Science - Information Theory
Abstract

Consider statistical learning (e.g. discrete distribution estimation) with local $\epsilon$-differential privacy, which preserves each data provider's privacy locally, we aim to optimize statistical data utility under the privacy constraints. Specifically, we study maximizing mutual information between a provider's data and its private view, and give the exact mutual information bound along with an attainable mechanism: $k$-subset mechanism as results. The mutual information optimal mechanism randomly outputs a size $k$ subset of the original data domain with delicate probability assignment, where $k$ varies with the privacy level $\epsilon$ and the data domain size $d$. After analysing the limitations of existing local private mechanisms from mutual information perspective, we propose an efficient implementation of the $k$-subset mechanism for discrete distribution estimation, and show its optimality guarantees over existing approaches., Comment: submitted to NIPS2016

Published
2016

10. Classification Learning from Private Data in Heterogeneous Settings

Author

Nie, Yiwen, Wang, Shaowei, Yang, Wei, Huang, Liusheng, Zhao, Zhenhua, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pei, Jian, editor, Manolopoulos, Yannis, editor, Sadiq, Shazia, editor, and Li, Jianxin, editor

Published
2018
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11. TRQED: Secure and Fast Tree-Based Private Range Queries over Encrypted Cloud

Author

Yang, Wei, Xu, Yang, Nie, Yiwen, Shen, Yao, Huang, Liusheng, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pei, Jian, editor, Manolopoulos, Yannis, editor, Sadiq, Shazia, editor, and Li, Jianxin, editor

Published
2018
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12. Geospatial Streams Publish with Differential Privacy

Author

Nie, Yiwen, Huang, Liusheng, Li, Zongfeng, Wang, Shaowei, Zhao, Zhenhua, Yang, Wei, Lu, Xiaorong, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Wang, Shangguang, editor, and Zhou, Ao, editor

Published
2017
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21. Anion exchange membranes based on poly (styrene‐b‐(ethylene‐co‐butylene)‐b‐styrene) grafted poly (2,6‐dimethyl‐1,4‐phenylene oxide).

Author

Liu, Rui, Nie, Yiwen, Chen, Junjie, Shen, Chunhui, and Gao, Shanjun

Subjects
ION-permeable membranes, POLYPHENYLENE oxide, ETHER synthesis, CHEMICAL stability, ION exchange (Chemistry), IONIC conductivity
Abstract

Poly (styrene‐b‐(ethylene‐co‐butylene)‐b‐styrene) (SEBS) has been widely studied in the field of anion exchange membranes (AEMs) due to its superior flexibility and chemical stability, but the poor dimensional stability of functionalized SEBS restricts its further application. Thus, we adopt an effective strategy to reinforce AEMs based on SEBS via grafting poly (2,6‐dimethyl‐1,4‐phenylene oxide) (PPO) using Williamson ether synthesis reaction. With the addition of PPO, grafted membranes exhibit outstanding comprehensive properties, such as mechanical strength, chemical stability, especially dimensional stability. SEBS‐PPO‐QA‐1 grafted membrane with the highest content of PPO showed the best tensile strength (20.54 MPa), and restrained swelling behavior (swelling ratio of 4.81% at 25°C). Moreover, the conductivity retention of SEBS‐PPO‐QA‐1 grafted membrane is up to 92.71% at 80°C after 10 days alkaline treatment. The ion exchange capacity of SEBS‐PPO‐QA‐3 grafted membrane is 1.719 mmol/g, and the ionic conductivity of SEBS‐PPO‐QA‐3 grafted membrane reach 58.27 mS/cm at 80°C and 25.53 mS/cm at 20°C. Therefore, the SEBS‐PPO‐QA‐X grafted membranes, which combine the advantages of PPO and SEBS, are promising in AEM application. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Anomalous Trajectory Detection Between Regions of Interest Based on ANPR System

Author

Xu Hongli, Huang Liusheng, Nie Yiwen, Yang Wei, and Gao Ying

Subjects
050210 logistics & transportation, business.industry, Computer science, 05 social sciences, 02 engineering and technology, computer.software_genre, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Global Positioning System, Trajectory, 020201 artificial intelligence & image processing, Data mining, business, computer, Abstraction (linguistics)
Abstract

With the popularization of automobiles, more and more algorithms have been proposed in the last few years for the anomalous trajectory detection. However, existing approaches, in general, deal only with the data generated by GPS devices, which need a great deal of pre-processing works. Moreover, without the consideration of region’s local characteristics, those approaches always put all trajectories even though with different source and destination regions together. Therefore, in this paper, we devise a novel framework for anomalous trajectory detection between regions of interest by utilizing the data captured by Automatic Number-Plate Recognition (ANPR) system. Our framework consists of three phases: abstraction, detection, classification, which is specially engineered to exploit both spatial and temporal features. In addition, extensive experiments have been conducted on a large-scale real-world datasets and the results show that our framework can work effectively.

Published
2018

39. A General Strategy for Assembling Nanoparticles in One Dimension

Author

Shuoran Chen, Lei Jiang, Xiaonan Kan, Nie Yiwen, Cong Zhang, Yuchen Wu, Linfeng Chen, Yanlin Song, Bin Su, and Xingye Zhang

Subjects
chemistry.chemical_compound, Materials science, Semiconductor quantum dots, Dimension (vector space), chemistry, Mechanics of Materials, Mechanical Engineering, Oxide, Nanoparticle, General Materials Science, Nanotechnology, Wafer, Microsphere
Abstract

Alignment of 1D assemblies of a wide variety of nanoparticles (e.g., metal, metal oxide, semiconductor quantum dots, or organic microspheres) in one direction upon diverse substrates (including industrial silicon wafers and transparent glass plates) by a general strategy is demonstrated. This sandwich method provides an efficient way of rapidly and precisely assembling nanoparticles on a large scale (up to 10 cm × 10 cm) for device applications.

Published
2014

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