This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online ide...This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.展开更多
A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cell...A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.展开更多
Recently there is an increasing need for interactive human-driven analysis on large volumes of data. Online aggregation （OLA）, which provides a quick sketch of massive data before a long wait of the final accurate q...Recently there is an increasing need for interactive human-driven analysis on large volumes of data. Online aggregation （OLA）, which provides a quick sketch of massive data before a long wait of the final accurate query result, has drawn significant research attention. However, the direct processing of OLA on duplicate data will lead to incorrect query answers, since sampling from duplicate records leads to an over representation of the duplicate data in the sample. This violates the prerequisite of uniform distributions in most statistical theories. In this paper, we propose CrowdOLA, a novel framework for integrating online aggregation processing with deduplication. Instead of cleaning the whole dataset, Crow~ dOLA retrieves block-level samples continuously from the dataset, and employs a crowd-based entity resolution approach to detect duplicates in the sample in a pay-as-you-go fashion. After cleaning the sample, an unbiased estimator is provided to address the error bias that is introduced by the duplication. We evaluate CrowdOLA on both real-world and synthetic workloads. Experimental results show that CrowdOLA provides a good balance between efficiency and accuracy.展开更多
针对车辆到电网(Vehicle to Grid,V2G)中进行跨域身份认证时容易造成实时性较差及隐私泄露的问题,文章提出了一种基于区块链的高效跨域身份认证方案。首先,该方案采用了在线/离线签名技术,在车辆空闲时进行离线签名,当车辆在其他区域进...针对车辆到电网(Vehicle to Grid,V2G)中进行跨域身份认证时容易造成实时性较差及隐私泄露的问题,文章提出了一种基于区块链的高效跨域身份认证方案。首先,该方案采用了在线/离线签名技术,在车辆空闲时进行离线签名,当车辆在其他区域进行跨域认证时只需在离线签名的基础上进行在线签名,降低了系统的计算开销,缩短了认证时间,并使用聚合签名及验证技术,引入批量验证的思想,提高了验证效率。然后,使用智能合约技术,在区块链上存储车辆公钥及离线签名等信息,降低了车辆的存储负担,也保证了密钥的安全性。最后,通过与其他方案对比和实验仿真,结果表明,该方案减少了签名及验证的时间开销,并且使用智能合约在区块链上存储信息的开销较小。基于q-Strong Diff ie-Hellman问题的难解性,证明了该方案是安全的,并且满足所有期望和安全要求。展开更多
Online aggregation is an attractive sampling-based technology to response aggregation queries by an estimate to the final result, with the confidence interval becoming tighter over time. It has been built into a MapRe...Online aggregation is an attractive sampling-based technology to response aggregation queries by an estimate to the final result, with the confidence interval becoming tighter over time. It has been built into a MapReduce-based cloud system for big data analytics, which allows users to monitor the query progress, and save money by killing the computation early once sufficient accuracy has been obtained. However, there are several limitations that restrict the performance of online aggregation generated from the gap between the current mechanism of MapHeduce paradigm and the requirements of online aggregation, such as： 1） the low sampling efficiency due to the lack of consideration of skewed data distribution for online aggregation in MapReduce, and 2） the large redundant I/O cost of online aggregation caused by the independent job execution mechanism of MapReduce. In this paper, we present OLACloud, a MapReduce-based cloud system to well support online aggregation for different data distributions and large-scale concurrent query processing. We propose a content-aware repartition method with a fair-allocation block placement strategy to increase the sampling efficiency and guarantee the storage and computation load balancing simultaneously. We also develop a shared sampling method to share the sampling opportunities among multiple queries to reduce redundant I/O cost. We also implement OLACloud in Hadoop, and conduct an extensive experimental study on the TPC-H benchmark for skewed data distribution. Our results demonstrate the efficiency and effectiveness of OLACloud.展开更多
基金supported by the State Grid Science&Technology Project(5100-202114296A-0-0-00).
文摘This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61871062and Grant 61771082the Natural Science Foundation of Chongqing of China under Grant cstc2013jcyjA40066+3 种基金the Program for Innovation Team Building at Institutions of Higher Education in Chongqing under Grant CXTDX201601020the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201801316the Key Industrial Technology Development Project of Chongqing of China Development and Reform Commission under Grant 2018148208the Innovation and Entrepreneurship Demonstration Team of Yingcai Program of Chongqing of China under Grant CQYC201903167.
文摘A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61502121, 61472099, and 61602129.
文摘Recently there is an increasing need for interactive human-driven analysis on large volumes of data. Online aggregation （OLA）, which provides a quick sketch of massive data before a long wait of the final accurate query result, has drawn significant research attention. However, the direct processing of OLA on duplicate data will lead to incorrect query answers, since sampling from duplicate records leads to an over representation of the duplicate data in the sample. This violates the prerequisite of uniform distributions in most statistical theories. In this paper, we propose CrowdOLA, a novel framework for integrating online aggregation processing with deduplication. Instead of cleaning the whole dataset, Crow~ dOLA retrieves block-level samples continuously from the dataset, and employs a crowd-based entity resolution approach to detect duplicates in the sample in a pay-as-you-go fashion. After cleaning the sample, an unbiased estimator is provided to address the error bias that is introduced by the duplication. We evaluate CrowdOLA on both real-world and synthetic workloads. Experimental results show that CrowdOLA provides a good balance between efficiency and accuracy.
文摘针对车辆到电网(Vehicle to Grid,V2G)中进行跨域身份认证时容易造成实时性较差及隐私泄露的问题,文章提出了一种基于区块链的高效跨域身份认证方案。首先,该方案采用了在线/离线签名技术,在车辆空闲时进行离线签名,当车辆在其他区域进行跨域认证时只需在离线签名的基础上进行在线签名,降低了系统的计算开销,缩短了认证时间,并使用聚合签名及验证技术,引入批量验证的思想,提高了验证效率。然后,使用智能合约技术,在区块链上存储车辆公钥及离线签名等信息,降低了车辆的存储负担,也保证了密钥的安全性。最后,通过与其他方案对比和实验仿真,结果表明,该方案减少了签名及验证的时间开销,并且使用智能合约在区块链上存储信息的开销较小。基于q-Strong Diff ie-Hellman问题的难解性,证明了该方案是安全的,并且满足所有期望和安全要求。
基金supported by the National Basic Research 973 Program of China under Grant No.2010CB328104the National Natural Science Foundation of China under Grant Nos.61070161,61202449,61320106007+4 种基金the National High Technology Research and Development 863 Program of China under Grant No.2013AA013503the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20110092130002the Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No.BM2003201the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No.93K-9the Shanghai Key Laboratory of Scalable Computing and Systems of China under Grant No.2010DS680095
文摘Online aggregation is an attractive sampling-based technology to response aggregation queries by an estimate to the final result, with the confidence interval becoming tighter over time. It has been built into a MapReduce-based cloud system for big data analytics, which allows users to monitor the query progress, and save money by killing the computation early once sufficient accuracy has been obtained. However, there are several limitations that restrict the performance of online aggregation generated from the gap between the current mechanism of MapHeduce paradigm and the requirements of online aggregation, such as： 1） the low sampling efficiency due to the lack of consideration of skewed data distribution for online aggregation in MapReduce, and 2） the large redundant I/O cost of online aggregation caused by the independent job execution mechanism of MapReduce. In this paper, we present OLACloud, a MapReduce-based cloud system to well support online aggregation for different data distributions and large-scale concurrent query processing. We propose a content-aware repartition method with a fair-allocation block placement strategy to increase the sampling efficiency and guarantee the storage and computation load balancing simultaneously. We also develop a shared sampling method to share the sampling opportunities among multiple queries to reduce redundant I/O cost. We also implement OLACloud in Hadoop, and conduct an extensive experimental study on the TPC-H benchmark for skewed data distribution. Our results demonstrate the efficiency and effectiveness of OLACloud.
文摘联机分析处理OLAP(online analytical processing)查询作为一种复杂查询,当使用SQL(structured query language)语句来表述时,通常都包含多表连接和分组聚集操作,因此提高多表连接和分组聚集计算的性能就成为ROLAP(relational OLAP)查询处理的关键问题.提出一种基于分组序号的聚集算法MuGA(group number based aggregation with multi-table join),该方法充分考虑数据仓库星型模式的特点,将聚集操作和新的多表连接算法MJoin(multi-table join)相结合,使用分组序号进行分组聚集计算,代替通常的排序或者哈希计算,从而有效地减少CPU运算以及磁盘存取的开销.算法的实验数据表明,提出的MuGA算法与传统的关系数据库聚集查询处理方法以及改进后的基于排序的聚集算法相比,性能都有显著提高.
