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Efficient protocols for heavy hitter identification with local differential privacy

Frontiers of Computer Science 2022年第5期16卷 193-203页

作者： Dan ZHAO Suyun ZHAO Hong CHEN Ruixuan LIU Cuiping LI Wenjuan LIANG Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of ChinaBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Local differential privacy(LDP),which is a technique that employs unbiased statistical estimations instead of real data,is usually adopted in data collection,as it can protect every user’s privacy and prevent the leakage of sensitive information.The segment pairs method(SPM),multiple-channel method(MCM)and prefix extending method(PEM)are three known LDP protocols for heavy hitter identification as well as the frequency oracle(FO)problem with large domains.However,the low scalability of these three LDP algorithms often limits their application.Specifically,communication and computation strongly affect their efficiency.Moreover,excessive grouping or sharing of privacy budgets makes the results inaccurate.To address the abovementioned problems,this study proposes independent channel(IC)and mixed independent channel(MIC),which are efficient LDP protocols for FO with a large domains.We design a flexible method for splitting a large domain to reduce the number of sub-domains.Further,we employ the false positive rate with interaction to obtain an accurate estimation.Numerical experiments demonstrate that IC outperforms all the existing solutions under the same privacy guarantee while MIC performs well under a small privacy budget with the lowest communication cost.

关键词： local differential privacy frequency oracle heavy hitter

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Efficient heavy hitters Identification over Speed Traffic Streams

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Computers, Materials & Continua 2020年第4期63卷 213-222页

作者： Shuzhuang Zhang Hao Luo Zhigang Wu Yanbin Sun Yuhang Wang Tingting Yuan Institute of Network Technology Beijing University of Posts and TelecommunicationsBeijingChina Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Inria Diana Sophia Antipolis-Mediterranee Sophia AntipolisFrance

With the rapid increase of link speed and network throughput in recent years,much more attention has been paid to the work of obtaining statistics over speed traffic streams.It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy.In this paper,we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters.This method is called EBF sketches.Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy.It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters.Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.

关键词： Traffic stream heavy hitter sliding window frequency statistics

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Identifying heavy hitters in high-speed network monitoring

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Science China(Information Sciences) 2010年第3期53卷 659-676页

作者： ZHANG Yu1, FANG BinXing1,2 & ZHANG YongZheng2 1Research Center of Computer Network and Information Security Technology, Harbin Institute of Technology, Harbin 150001, China 2Research Center of Information Security, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China Research Center of Computer Network and Information Security Technology Harbin Institute of Technology Harbin China Research Center of Information Security Institute of Computing Technology Chinese Academy of Sciences Beijing China

Identifying heavy hitters in a network traffic stream is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial-of-service attacks. Existing methods generally examine newly arriving items in the stream, perform a small number of operations using a small amount of memory, and still provide guarantees on the identifying accuracy. In high-speed network monitoring, the update speed per item is extremely critical. However, so far as we know, there are no identifying algorithms which can provide constant update time (O(1)) in a weighted data stream. In this paper, we present an algorithm named Weighted Lossy Counting (WLC) which is able to identify heavy hitters in a high-speed weighted data stream with constant update time. WLC employs a novel efficient partially ordered data structure which is able to provide a fast per-item update speed while keeping the memory cost relatively low. We compare WLC with state-of-the-art algorithms for finding heavy hitters in real traffic traces. The experimental results show that WLC performs well in accuracy (recall, precision and average relative error) as other algorithms; moreover it has a much higher update speed at the cost of relatively larger memory space used. A theoretical worst-case memory bound of WLC is also derived in this paper; however, experiments with long real traffic traces show that WLC requires much less space than the theoretical bound in practice.

关键词： network traffic monitoring heavy hitter weighted data streams

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