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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Filic, Mia Kocher, Keran Kummer, Ella Unnikrishnan, Anupama Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9789819608935;9789819608942

probabilistic data structures (PDS) are compact representations of high-volume data that provide approximate answers to queries about the data. They are commonplace in today's computing systems, finding use in databases, networking and more. While PDS are designed to perform well under benign inputs, they are frequently used in applications where inputs may be adversarially chosen. This may lead to a violation of their expected behaviour, for example an increase in false positive rate. In this work, we focus on PDS that handle approximate membership queries (AMQ). We consider adversarial users with the capability of making adaptive insertions, deletions and membership queries to AMQ-PDS, and analyse the performance of AMQ-PDS under such adversarial inputs. We argue that deletions significantly empower adversaries, presenting a challenge to enforcing honest behaviour when compared to insertion-only AMQ-PDS. To address this, we introduce a new concept of an honest setting for AMQ-PDS with deletions. By leveraging simulation-based security definitions, we then quantify how much harm can be caused by adversarial users to the functionality of AMQ-PDS. Our resulting bounds only require calculating the maximal false positive probability and insertion failure probability achievable in our novel honest setting. We apply our results to Cuckoo filters and Counting filters. We show how to protect these AMQ-PDS at low cost, by replacing or composing the hash functions with keyed pseudorandom functions in their construction. This strategy involves establishing practical bounds for the probabilities mentioned above. Using our new techniques, we demonstrate that achieving security against adversarial users making both insertions and deletions remains practical.

关键词： probabilistic data structures Counting filters Cuckoo filters security simulation-based proofs

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A probabilistic data structures-Based Anomaly Detection Scheme for Software-Defined Internet of Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第6期22卷 3557-3566页

作者： Garg, Sahil Singh, Amritpal Aujla, Gagangeet Singh Kaur, Sukhdeep Batra, Shalini Kumar, Neeraj Univ Quebec Elect Engn Dept Ecole Technol Super Montreal PQ H3C 1K3 Canada Natl Inst Technol Comp Sci & Engn Dept Jalandhar 144805 Punjab India Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG Tyne & Wear England Chandigarh Univ Comp Sci & Engn Dept Mohali 140413 India Thapar Inst Engn & Technol Patiala 147001 Punjab India Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan King Abdulaziz Univ Jeddah 21589 Saudi Arabia

Internet of Vehicles (IoV) has escalated the movement of big data across moving vehicles which create a huge burden on the network infrastructure. In IoV environment, effective handling of streaming data has to face various challenges like;traffic monitoring, flow management, re-configuration and security. Software-defined networks (SDN) provides improved flexibility, and centralized control of the network to overcome (almost) the above-mentioned challenges. However, it can lead to an easy target (node or controller) for malicious agents. So, to detect the anomalous behaviour of the nodes in the IoV environment, a hybrid approach using probabilistic data structures is proposed which works in the following phases. In phase I, a traffic monitoring scheme using Count-Min-Sketch is designed to identify the suspicious nodes. In phase II, to detect an anomaly, a Bloom filter-based control scheme is used for signature verification of suspicious nodes. In phase III, a Quotient filter is used for fast and efficient storage of malicious nodes. In phase IV, to detect the super points (malicious hosts that are connected to a large number of destinations), a Hyperloglog counter is used to measure the cardinality of each flow passing through the switches. The proposed scheme has been evaluated in a simulated environment. The results obtained depict that the proposed scheme is faster, accurate, and efficient concerning detection ratio and false-positive ratio.

关键词： Monitoring probabilistic logic Internet Control systems Denial-of-service attack Anomaly detection Anomaly detection probabilistic data structures Software-defined networks Internet of Vehicles

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probabilistic data structures for big data analytics: A comprehensive review

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷

作者： Singh, Amritpal Garg, Sahil Kaur, Ravneet Batra, Shalini Kumar, Neeraj Zomaya, Albert Y. Deemed Univ Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India Univ Sydney Ctr Distributed & High Performance Comp Sydney NSW Australia

An exponential increase in the data generation resources is widely observed in last decade, because of evolution in technologies such as-cloud computing, IoT, social networking, etc. This enormous and unlimited growth of data has led to a paradigm shift in storage and retrieval patterns from traditional data structures to probabilistic data structures (PDS). PDS are a group of data structures that are extremely useful for Big data and streaming applications in order to avoid high-latency analytical processes. These data structures use hash functions to compactly represent a set of items in stream-based computing while providing approximations with error bounds so that well-formed approximations get built into data collections directly. Compared to traditional data structures, PDS use much less memory and constant time in processing complex queries. This paper provides a detailed discussion of various issues which are normally encountered in massive data sets such as-storage, retrieval, query,etc. Further, role of PDS in solving these issues is also discussed where these data structures are used as temporary accumulators in query processing. Several variants of existing PDS along with their application areas have also been explored which give a holistic view of domains where these data structures can be applied for efficient storage and retrieval of massive data sets. Mathematical proofs of various parameters considered in the PDS have also been discussed in the paper. Moreover, the relative comparison of various PDS with respect to various parameters is also explored. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Big data Internet of things (IoT) probabilistic data structures Bloom filter Quotient filter Count min sketch HyperLogLog counter MM-hash Locality sensitive hashing

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ProIDS: probabilistic data structures based Intrusion Detection System for Network Traffic Monitoring

<i>ProIDS</i>: Probabilistic Data Structures based Intrusion...

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IEEE Global Telecommunications Conference (GLOBECOM)

作者： Gupta, Divya Garg, Sahil Singh, Amritpal Batra, Shalini Kumar, Neeraj Obaidat, M. S. Thapar Univ Comp Sci & Engn Dept Patiala Punjab India Univ Jordan King Abdullah Sch Informat Technol Amman Jordan

ISBN: (纸本)9781509050192

Internet is an integrated platform where the data is growing at an exponential rate. Since it incorporates numerous business and personal services, we need to protect the data from illegal access or modifications. In literature, a large number of techniques have been proposed to protect the data against the malicious intent of the intruders. However, one of the most important way for monitoring and analyzing network traffic against various attacks is by the deployment of Intrusion detection systems (IDS). This paper presents a novel IDS based on probabilistic data structures named as ProIDS. In the proposed ProIDS, a popular probabilistic data structure (PDS), Bloom filter has been used to store the information about the suspicious nodes. Using Bloom filter, the number of hits on suspicious nodes per unit time has been computed using the modified version of Count min sketch, i.e., MCMS, a PDS. The work also presents a detailed theoretical analysis backed by relevant technical description. Simulation results clearly depict that the proposed system is more reliable and scalable in comparison to the existing Count-min sketch method. The results obtained show that proposed system requires comparatively less computational time and storage in comparison to the existing Count-min sketch method.

关键词： probabilistic data structures Bloom filter Count-min sketch Intrusion detection Network security

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Automated Expected Amortised Cost Analysis of probabilistic data structures 34th

Automated Expected Amortised Cost Analysis of Probabilistic ...

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34th International Conference on Computer-Aided Verification (CAV) held as part of the Federated Logic Conference (FLoC)

作者： Leutgeb, Lorenz Moser, Georg Zuleger, Florian Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Grad Sch Comp Sci Saarland Informat Campus Saarbrucken Germany Univ Innsbruck Dept Comp Sci Innsbruck Austria Tech Univ Wien Inst Log & Computat 192 4 Vienna Austria

ISBN: (纸本)9783031131882;9783031131875

In this paper, we present the first fully-automated expected amortised cost analysis of self-adjusting data structures, that is, of randomised splay trees, randomised splay heaps and randomised meldable heaps, which so far have only (semi-)manually been analysed in the literature. Our analysis is stated as a type-and-effect system for a first-order functional programming language with support for sampling over discrete distributions, non-deterministic choice and a ticking operator. The latter allows for the specification of fine-grained cost models. We state two soundness theorems based on two different-but strongly related-typing rules of ticking, which account differently for the cost of non-terminating computations. Finally we provide a prototype implementation able to fully automatically analyse the aforementioned case studies.

关键词： amortised cost analysis functional programming probabilistic data structures automation constraint solving

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LSM tree read-deletion operations optimization through the implementation of cuckoo filters

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JOURNAL OF BIG data 2025年第1期12卷 1-20页

作者： Valverde, Humberto Cesar Villalta Kim, Kwangsik Kim, Kisu Kwon, Jinman Lim, Jaechoon Jun, Yongjoo Dtonic Seongnam 13591 Gyeonggido South Korea

This study aims to develop an LSM tree that uses two different kinds of probabilistic data structures (PDS). These two data structures are the Bloom Filters and the state-of-the-art Cuckoo Filter released in 2014 by Fan. Cuckoo filters are a perfect choice for saving space and also for deleting keys that are not needed in the algorithm so the filters are synchronized with the keys of all the SSTables. The implementation in this study of Cuckoo filters showed three main advantages (1) Keys that are deleted in the SSTables are immediately reflected in the Cuckoo Filters (2) When SSTables are merged, Cuckoo Filters are also merged by measuring its load factor for availability (3) The performance of Cuckoo Filters compared to Bloom filters after deleting and query shows better performance despite that cuckoo filters are also being updated. This approach is expected to reduce storage space and the amount of time for queries and deletions on the overall data structure. This new optimized LSM tree is expected to be implemented in HBase as future work to check its performance with a real-world database for more empirical results.

关键词： probabilistic data structures Indexing algorithms Query optimization Cuckoo filters Bloom filters

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EPMDroid: Efficient and privacy-preserving malware detection based on SGX through data fusion

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INFORMATION FUSION 2022年 82卷 43-57页

作者： Wei, Wentao Wang, Jie Yan, Zheng Ding, Wenxiu Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian Peoples R China Aalto Univ Dept Commun & Networking Espoo Finland

Android has stood at a predominant position in mobile operating systems for many years. However, its popularity and openness make it a desirable target of malicious attackers. There is an increasing need for mobile malware detection. Existing analysis methods fall into two categories, i.e., static analysis and dynamic analysis. The dynamic analysis is more effective and timely than the static one, but it incurs a high computational overhead, thus cannot be deployed in resource-constrained mobile devices. Existing studies solve this issue by outsourcing malware detection to the cloud. However, the privacy of mobile app runtime data uploaded to the cloud is not well preserved during both detection model training and malware detection. Numerous efforts have been made to preserve privacy with cryptography, which suffers from high computational overhead and low flexibility. To address these issues, in this paper, we propose an Intel SGXempowered mobile malware detection scheme called EPMDroid. We also design a probabilistic data structure based on cuckoo filters, named CuckooTable, to effectively fuse features for detection and achieve high space efficiency. We conduct both theoretical analysis and real-world data based tests on EPMDroid performance. Experimental results show that EPMDroid can speed up malware detection by up to 43.8 times and save memory space by up to 3.7 times with the same accuracy, as compared to a baseline method.

关键词： Malware detection Intel SGX Privacy preservation probabilistic data structures data fusion

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Privacy preservation in outsourced mobility traces through compact data structures

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2020年 55卷

作者： Calderoni, Luca Bandini, Samantha Maio, Dario Univ Bologna Dept Comp Sci & Engn Bologna Italy

Indoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user's privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment.

关键词： Privacy in outsourced data probabilistic data structures Privacy metrics Location-based services GDPR Indoor localization

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Bloom filter based optimization scheme for massive data handling in IoT environment

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 82卷 440-449页

作者： Singh, Amritpal Garg, Sahil Batra, Shalini Kumar, Neeraj Rodrigues, Joel J. P. C. Deemed Univ Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India Natl Inst Telecommun Inatel Santa Rita Do Sapucai MG Brazil Inst Telecomunicacoes Santa Rita Do Sapucai MG Brazil ITMO Univ St Petersburg Russia Univ Fortaleza UNIFOR Fortaleza Ceara Brazil

With the widespread popularity of big data usage across various applications, need for efficient storage, processing, and retrieval of massive datasets generated from different applications has become inevitable. Further, handling of these datasets has become one of the biggest challenges for the research community due to the involved heterogeneity in their formats. This can be attributed to their diverse sources of generation ranging from sensors to on-line transactions data and social media access. In this direction, probabilistic data structures (PDS) are suitable for large-scale data processing, approximate predictions, fast retrieval and unstructured data storage. In conventional databases, entire data needs to be stored in memory for efficient processing, but applications involving real time in-stream data demand time-bound query output in a single pass. Hence, this paper proposes Accommodative Bloom filter (ABF), a variant of scalable bloom filter, where insertion of bulk data is done using the addition of new filters vertically. Array of m bits is divided into b buckets of l bits each and new filters of size 'm/k' are added to each bucket to accommodate the incoming data. data generated from various sensors has been considered for experimental purposes where query processing is done at two levels to improve the accuracy and reduce the search time. It has been found that insertion and search time complexity of ABF does not increase with increase in number of elements. Further, results indicate that ABF outperforms the existing variants of Bloom filters in terms of false positive rates and query complexity, especially when dealing with in-stream data. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Internet of Things Big data analytics probabilistic data structures Bloom filter In-stream data processing

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probabilistic data structure-based community detection and storage scheme in online social networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 94卷 173-184页

作者： Singh, Amritpal Garg, Sahil Batra, Shalini Kumar, Neeraj Deemed Univ Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala Punjab India Univ Quebec ETS Elect Engn Dept Montreal PQ H3C 1K3 Canada

With the wide popularity of various social network sites such as - Facebook, Twitter, and Instagram;processing, storage, and analysis of a large volume of data are becoming challenging issues. In general, social networks are assumed to be graphs having nodes for representation of a group of persons in order to explore the relationship between them for Social Network Analysis (SNA). Analysts claim that interconnections in these networks are the reflection of social structure of individual where personalities with common attributes often occupy similar positions. Such similarities are caused by the prospects, opportunities, sensitivities and perceptions created by these similar network positions. Thus, clustering of these individuals is necessary to analyse their common characteristics. However, most of the existing clustering algorithms considered for community detection in SNA have high memory requirements especially in online social networks. So, to mitigate these issues, this paper proposes a novel clustering scheme for community detection for fast access, storage and retrieval of data using probabilistic data structures (PDS). In the proposed scheme, Bloom filter has been used for clustering and Quotient filter has been used for storage and access of cluster nodes. It has been experimentally proved that the proposed scheme provides significant improvement in computational time which is reduced by 64% and 79% respectively in comparison to the linked list and adjacency matrix. Moreover, Quotient filter based storage schema significantly enhances the effectiveness of the proposed scheme over conventional storage methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Social network analysis Community detection Graph storage probabilistic data structures Bloom filter Quotient filter

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