Scalable Preference Learning from Data Streams

Dzogang, Fabon; Lansdall-Welfare, Thomas; Sudhahar, Saatviga; Cristianini, Nello

doi:10.1145/2740908.2742008

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Frequency estimation has also been a fundamental building block of many other sophisticated data analysis and machine learning techniques such as ranking [4], feature selection [5], and natural language processing [6]. Despite the usefulness of frequency estimation and data analysis for decision-making process, if it is not done with care, such practice can violate the privacy of users' sensitive information, which has been a more important concern for users.…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Distributed Frequency Estimation

Yang

Tjuawinata

Lam

et al. 2023

IEEE Trans. Dependable and Secure Comput.

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In order to remain competitive, Internet companies collect and analyse user data for the purpose of the improvement of user experiences. Frequency estimation is a widely used statistical tool, which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which require either a large user base or a trusted server. Although the requirements for such solutions may not be a problem for larger companies, they may be unattainable for smaller organizations. To address this issue, we propose a distributed privacy-preserving sampling-based frequency estimation method which has high accuracy even in the scenario with a small number of users while not requiring any trusted server. This is achieved by combining multi-party computation and sampling techniques. We also provide a relation between its privacy guarantee, output accuracy, and the number of participants. Distinct from most existing methods, our methods achieve centralized differential privacy guarantee without the need of any trusted server. We established that, even for a small number of participants, our mechanisms can produce estimates with high accuracy and hence they provide smaller companies with more opportunity for growth through privacy-preserving statistical analysis. We further propose an architectural model to support weighted aggregation in order to achieve a higher accuracy estimate to cater for users with varying privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Differentially Private Distributed Frequency Estimation

Yang

Tjuawinata

Lam

et al. 2023

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

show abstract

“…Estimating item frequency is a basic topic in data stream processing, which finds applications in the fields of networking, databases, and machine learning, such as real-time data analyzing (Weller 2018;Zhu and Shasha 2002;Tinati et al 2015;Irfan and Gordon 2019), network traffic monitoring (Huang, Lee, and Bao 2018;Madden and Franklin 2002;Wang et al 2013), natural language processing (Goyal, III, and Cormode 2012) and search ranking (Dzogang et al 2015). Towards infinite data streams, a common class of solutions (Cormode and Muthukrishnan 2005;Charikar, Chen, and Farach-Colton 2002;Estan and Varghese 2002;Roy, Khan, and Alonso 2016;Zhou et al 2018;Hsu et al 2019) use a compact structure taking sublinear space for counting the number of occurrences of each stream item, called the sketch.…”

Section: Introductionmentioning

confidence: 99%

Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams

Cao

Feng

Xie

2023

AAAI

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To estimate item frequencies of data streams with limited space, sketches are widely used in real applications, including real-time web analytics, network monitoring, and self-driving. Sketches can be viewed as a model which maps the identifier of a stream item to the corresponding frequency domain. Starting from the premise, we envision a neural data structure, which we term the meta-sketch, to go beyond the basic structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase, and can be fast adapted to real (skewed) distributions in the adaption phase. Extensive experiments demonstrate the performance gains of the meta-sketch and offer insights into our proposals.

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Section: Introductionmentioning

confidence: 99%

Fair and Differentially Private Distributed Frequency Estimation

Yang,

Tjuawinata,

Lam

et al. 2021

Preprint

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In order to remain competitive, Internet companies collect and analyse user data for the purpose of improving user experiences. Frequency estimation is a widely used statistical tool which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which either require a large user base or a trusted server; hence may give big companies an unfair advantage while handicapping smaller organizations in their growth opportunity. To address this issue, this paper proposes a fair privacy-preserving sampling-based frequency estimation method and provides a relation between its privacy guarantee, output accuracy, and number of participants. We designed decentralized privacy-preserving aggregation mechanisms using multi-party computation technique and established that, for a limited number of participants and a fixed privacy level, our mechanisms perform better than those that are based on traditional perturbation methods; hence, provide smaller companies a fair growth opportunity. We further propose an architectural model to support weighted aggregation in order to achieve higher accuracy estimate to cater for users with different privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.

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Scalable Preference Learning from Data Streams

Cited by 7 publications

References 15 publications

Differentially Private Distributed Frequency Estimation

Differentially Private Distributed Frequency Estimation

Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams

Fair and Differentially Private Distributed Frequency Estimation

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