2017 51st Asilomar Conference on Signals, Systems, and Computers最新文献_第3页

Bayesian time series matching and privacy 贝叶斯时间序列匹配与隐私

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335645

Ke Li, H. Pishro-Nik, D. Goeckel

{"title":"Bayesian time series matching and privacy","authors":"Ke Li, H. Pishro-Nik, D. Goeckel","doi":"10.1109/ACSSC.2017.8335645","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335645","url":null,"abstract":"A user's privacy can be compromised by matching the statistical characteristics of an anonymized trace of interest to prior behavior of the user. Here, we address this matching problem from first principles in the Bayesian case, where user parameters are drawn from a known distribution, to understand the relationship between the length of the observed traces, the characteristics of the distribution defining the differences between user behavior, and user privacy. First, we establish optimal tests (of two hypotheses and extended to multiple hypotheses as well) for the cases with: 1) continuous alphabets, in particular i.i.d. Gaussian observations with a different (unknown) mean for each user, where the means are drawn from a general a priori distribution; 2) binary alphabets where i.i.d. observations are drawn from a Bernoulli distribution, with each user having an (unknown) probability of being in the \"0\" state drawn from some certain a priori distribution. Next, for the case with Gaussian observations, we provide general (non-asymptotic) bounds to the performance of the tests and also employ these to show the scaling behavior of privacy. Finally, we present simulation results to demonstrate the accuracy of our analytical bounds.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126163407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Conjugate gradients based stochastic adaptive filters 基于共轭梯度的随机自适应滤波器

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335621

C. Radhakrishnan, A. Singer

引用次数: 0

Detection of almost-cyclostationarity: An approach based on a multiple hypothesis test 近乎循环平稳性的检测:一种基于多重假设检验的方法

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335636

Stefanie Horstmann, D. Ramírez, P. Schreier

引用次数: 2

Novel combinatorial coding results for DNA sequencing and data storage 用于DNA测序和数据存储的新型组合编码结果

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335392

Clayton Schoeny, Frederic Sala, L. Dolecek

引用次数: 21

A graph diffusion LMS strategy for adaptive graph signal processing 一种自适应图信号处理的图扩散LMS策略

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335711

Roula Nassif, C. Richard, Jie Chen, A. H. Sayed

引用次数: 12

Robust real-time sound pressure level stabilizer for multi-channel hearing aids compression for dynamically changing acoustic environment 鲁棒实时声压级稳定器，用于多通道助听器压缩动态变化的声环境

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335706

Yiya Hao, Ram Charan, G. Bhat, I. Panahi

引用次数: 5

From massive MIMO to C-RAN: The OpenAirInterface 5G testbed 从大规模MIMO到C-RAN: OpenAirInterface 5G测试平台

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335413

F. Kaltenberger, Xiwen Jiang, R. Knopp

{"title":"From massive MIMO to C-RAN: The OpenAirInterface 5G testbed","authors":"F. Kaltenberger, Xiwen Jiang, R. Knopp","doi":"10.1109/ACSSC.2017.8335413","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335413","url":null,"abstract":"5G will be much more than just a new radio interface — 5G will fundamentally change the way networks are operated. Traditional telecoms equipment will be replaced successively with general purpose computing platforms. This change will affect both the core network and the radio network, where it is also called cloud-RAN (C-RAN). The C-RAN architecture allows for flexible splits between different processing elements in the radio network, for an optimal trade-off between processing in the cloud or in the remote radio unit (RRU). OpenAirInterface (OAI) is an open source initiative that today provides Rel-8/Rel-10 3GPP compliant reference implementation of eNodeB, UE, RRH and EPC that runs on general purpose computing platforms. Already today OAI offers several functional splits for its 4G radio stack, for example between the Radio Cloud Center (RCC) and a Remote Radio Unit (RRU). Moreover, Eurecom is currently deploying a C-RAN network at its premises in Sophia-Antipolis, France, using a low-cost solution for RRU based on off-the-shelf equipment. In this paper we are going to describe the OAI C-RAN architecture with a special emphasis on the possibilities to do multi-cell distributed MIMO processing. In particular we will show how to we can apply our recently developed TDD reciprocity calibration scheme to this distributed setting and integrate it seamlessly into the normal LTE operation.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Sketched covariance testing: A compression-statistics tradeoff 粗略协方差测试:一种压缩与统计的权衡

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335428

Gautam Dasarathy, P. Shah, Richard Baraniuk

引用次数: 0

Wideband spectrum sensing measurement results using tunable front-end and FPGA implementation 宽带频谱传感测量结果采用可调前端和FPGA实现

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335389

Xusong Wang, S. Chaudhari, M. Laghate, D. Cabric

引用次数: 5

Sensor selection and power allocation via maximizing Bayesian fisher information for distributed vector estimation 基于最大贝叶斯fisher信息的分布式矢量估计传感器选择和功率分配

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-01 DOI: 10.1109/ACSSC.2017.8335580

Mojtaba Shirazi, Alireza Sani, A. Vosoughi

引用次数: 5