SAQE

Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications.

Agile analytics can help organizations to gain and sustain a competitive advantage by making timely decisions. Approximate query processing (AQP) is one of the useful approaches in agile analytics, which facilitates fast queries on big data by leveraging a pre-computed sample. One problem such a sample faces is that when new data is being imported, re-sampling is most likely needed to keep the sample fresh and AQP results accurate enough. Re-sampling from scratch for every batch of new data, called the full re-sampling method and adopted by many existing AQP works, is obviously a very costly process, and a much quicker incremental sampling process, such as reservoir sampling, may be used to cover the newly arrived data. However, incremental update methods suffer from the fact that the sample size cannot be increased, which is a problem when the underlying data distribution dramatically changes and the sample needs to be enlarged to maintain the AQP accuracy. This paper proposes an adaptive sample update (ASU) approach that avoids re-sampling from scratch as much as possible by monitoring the data distribution, and uses instead an incremental update method before a re-sampling becomes necessary. The paper also proposes an enhanced approach (T-ASU), which tries to enlarge the sample size without re-sampling from scratch when a bit of query inaccuracy is tolerable to further reduce the sample update cost. These two approaches are integrated into a state-of-the-art AQP engine for an extensive experimental study. Experimental results on both real-world and synthetic datasets show that the two approaches are faster than the full re-sampling method while achieving almost the same AQP accuracy when the underlying data distribution continuously changes.

Machine learning as a service (MLaaS) enables scalable model deployment and inference on cloud servers. However, MLaaS exposes user queries and model parameters to servers. To guarantee confidentiality of queries and model parameters, multi-party computation (MPC) enables secure inference by distributing data and computations across multiple service providers. MPC eliminates single points of failure, mitigates provider breaches and ensures confidentiality beyond legal agreements. Beyond confidentiality of queries and parameters, the model itself can memorize and leak training data during inference. To mitigate privacy concerns, differential privacy (DP) provides a formal privacy guarantee for training data, which can be satisfied by injecting carefully calibrated noise into gradients during training. However, naive combinations of DP and MPC amplify accuracy loss due to DP noise and MPC approximations, and incur high computational and communication overhead due to cryptographic operations. We present SPRINT, the first scalable solution for efficient MPC inference on DP fine-tuned models with high accuracy. SPRINT fine-tunes public pre-trained models on private data using DP. It integrates DP-specific optimizations, e.g., parameter-efficient fine-tuning and noise-aware optimizers, with MPC optimizations, e.g., cleartext public parameters and efficient approximations of non-linear functions. We evaluate SPRINT on GLUE benchmark with RoBERTa, achieving up to 1.6x faster MPC inference than the state-of-the-art non-DP solution SHAFT, reducing communication by 1.6x. Notably, SPRINT maintains high accuracy during MPC inference, with <1 percentage point gap compared to cleartext accuracy.

The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k ‐means algorithm and l ‐diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k ‐anonymous algorithm, the users' query privacy protection algorithm builds a query k ‐anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k ‐anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.

Approximate query processing (AQP) technique speeds up query execution by reducing the amount of data that needs to be processed, while sacrificing the accuracy of the query result to some extent. AQP is essentially a trade-off between the accuracy of the query result and the query latency. However, the heuristic AQP optimization and error control mechanism used by the existing AQP system fails to meet the accuracy requirements of users. This paper proposes a deep learning-based error prediction model to guide AQP query optimization. By using this model, we can estimate the errors of candidate query plans and select the appropriate plans that can meet the accuracy requirement with high probability. Extensive experiments show that the AQP system proposed in this paper can outperform the state-of-the-art online sampling-based AQP approach.

Differential privacy is an approach that preserves patient privacy while permitting researchers access to medical data. This paper presents mechanisms proposed to satisfy differential privacy while answering a given workload of range queries. Representing input data as a vector of counts, these methods partition the vector according to relationships between the data and the ranges of the given queries. After partitioning the vector into buckets, the counts of each bucket are estimated privately and split among the bucket's positions to answer the given query set. The performance of the proposed method was evaluated using different workloads over several attributes. The results show that partitioning the vector based on the data can produce more accurate answers, while partitioning the vector based on the given workload improves privacy. This paper's two main contributions are: (1) improving earlier work on partitioning mechanisms by building a greedy algorithm to partition the counts' vector efficiently, and (2) its adaptive algorithm considers the sensitivity of the given queries before providing results.

Differential privacy has garnered significant attention in recent years due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of queries while maintaining differential privacy has become the holy grail in privacypreserving query release. Towards this goal, this article surveys recent advances in query evaluation under differential privacy.

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost -- violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

Differential Privacy (DP) is a widely used technique for protecting individuals' privacy by limiting what can be inferred about them from aggregate data. Recently, there have been efforts to implement DP using Secure Multi-Party Computation (MPC) to achieve high utility without the need for a trusted third party. One of the key components of implementing DP in MPC is noise sampling. Our work presents the first MPC solution for sampling discrete Gaussian, a common type of noise used for constructing DP mechanisms, which plays nicely with malicious secure MPC protocols.

This study addresses the challenge of extracting valuable information and selecting key variables from large datasets, essential across statistics, computational science, and data science. In the age of big data, where safeguarding personal privacy is paramount, this study presents an online learning algorithm that leverages differential privacy to handle large‐scale data effectively. The focus is on enhancing the online group lasso approach within the differential privacy realm. The study begins by comparing online and offline learning approaches and classifying common online learning techniques. It proceeds to elucidate the concept of differential privacy and its importance. By enhancing the group‐follow‐the‐proximally‐regularized‐leader (GFTPRL) algorithm, we have created a new method for the online group lasso model that integrates differential privacy for binary classification in logistic regression. The research offers a solid validation of the algorithm’s effectiveness based on differential privacy and online learning principles. The algorithm’s performance was thoroughly evaluated through simulations with both synthetic and actual data. The comparison is made between the proposed privacy‐preserving algorithm and traditional non‐privacy‐preserving counterparts, with a focus on regret bounds, a measure of performance. The findings underscore the practical benefits of the differential privacy‐preserving algorithm in tackling large‐scale data analysis while upholding privacy standards. This research marks a significant step forward in the fusion of big data analytics and the safeguarding of individual privacy.

This work addresses the problem of maintaining privacy in range counting queries, with a focus on rolling-window queries over sensitive data. By employing differential privacy (DP), we can protect sensitive data used in these queries, as demonstrated in applications like COVID-19 tracking. Our research, however, identifies significant limitations in existing DP methods for small to medium-sized windows or ranges, which are commonly found in practical applications. We introduce a novel combinatorial interval covering approach, providing a detailed analysis of privacy-utility trade-offs within the DP framework. Our characterization of interval covering families’ thickness provides new insights into achieving $$\varepsilon $$ ε -DP with minimal error. Additionally, we propose a search-based method for selecting optimal families for differentially private range counting queries, specifically for small to medium windows, which outperforms current solutions in relevant scenarios. This work contributes to both the theoretical foundation and practical application of differential privacy in data analytics, ensuring robust privacy protection without compromising query accuracy.

Over the last decade, differential privacy (DP) has emerged as the gold standard of a rigorous and provable privacy framework. However, there are very few practical guidelines on how to apply differential privacy in practice, and a key challenge is how to set an appropriate value for the privacy parameter ɛ. In this work, we employ a statistical tool called hypothesis testing for discovering useful and interpretable guidelines for the state-of-the-art privacy-preserving frameworks. We formalize and implement hypothesis testing in terms of an adversary’s capability to infer mutually exclusive sensitive information about the input data (such as whether an individual has participated or not) from the output of the privacy-preserving mechanism. We quantify the success of the hypothesis testing using the precision- recall-relation, which provides an interpretable and natural guideline for practitioners and researchers on selecting ɛ. Our key results include a quantitative analysis of how hypothesis testing can guide the choice of the privacy parameter ɛ in an interpretable manner for a differentially private mechanism and its variants. Importantly, our findings show that an adversary’s auxiliary information - in the form of prior distribution of the database and correlation across records and time - indeed influences the proper choice of ɛ. Finally, we also show how the perspective of hypothesis testing can provide useful insights on the relationships among a broad range of privacy frameworks including differential privacy, Pufferfish privacy, Blowfish privacy, dependent differential privacy, inferential privacy, membership privacy and mutual-information based differential privacy.

Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. To build such an AQP system, finding the minimal sample size for a query regarding given error constraints in general, called Sample Size Optimization (SSO), is an essential yet unsolved problem. Ideally, the goal of solving the SSO problem is to achieve statistical accuracy, computational efficiency and broad applicability all at the same time. Existing approaches either make idealistic assumptions on the statistical properties of the query, or completely disregard them. This may result in overemphasizing only one of the three goals while neglect the others. To overcome these limitations, we first examine carefully the statistical properties shared by common analytical queries. Then, based on the properties, we propose a linear model describing the relationship between sample sizes and the approximation errors of a query, which is called the error model. Then, we propose a Model-guided Iterative Sample Selection (MISS) framework to solve the SSO problem generally. Afterwards, based on the MISS framework, we propose a concrete algorithm, called $$L^{2}\textsc{Miss}$$ , to find optimal sample sizes under the $$L^{2}$$ norm error metric. Moreover, we extend the $$L^{2}\textsc{Miss}$$ algorithm to handle other error metrics. Finally, we show theoretically and empirically that the $$L^{2}\textsc{Miss}$$ algorithm and its extensions achieve satisfactory accuracy and efficiency for a considerably wide range of analytical queries.

Neuromorphic computing, which is based on spiking neural networks (SNNs) and event cameras, can provide energy-efficient autonomous vehicle (AV) perception, yet is exceedingly susceptible to adversarial perturbations, fault injections, and data poisoning. Conventional defences may prove inadequate on the spot scenarios with a small amount of edge resources. The systemic security solution proposed in the paper consists of a Hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) attack detection model with a Differential Privacy (DP) and Secure Multi-Party Computation (SMPC) solution to privacy and threat-reduction respectively. Quantization and pruning are also used to optimise the framework to support edge deployment. KITTI multimodal experiment results indicate 94.3 percent accuracy and lower the attack success rate by 30 percent. The neuromorphic N-Caltech101 experiments reach an accuracy of 92.4 percent with a drop of 27 percent. These results confirm that the proposed solution can offer substantial, privacy-conscious and resource-efficient security of next-generation neuromorphic autonomous systems against trained adversarial attacks.

Efficient array storage is the backbone of scientific data processing. With an explosion of data, rapidly answering queries on array data is becoming increasingly important. Although most of the array storages today support subsetting of an array based on dimensions efficiently, they fall back to full scan while executing value-based filter operations. This has lead to an interest in approximate query processing, but such methods can have substantial inaccuracies.This paper presents COMPASS, an array storage system with integrated value index support. Our approach efficiently encodes arrays as bin-based indices and corresponding residuals describing elements in each bin. Our query processing method uses bin-based indices, with residuals decompressed as needed, to ensure that accuracy is not sacrificed. Our evaluation shows that compared with current array storage systems such as SciDB, our method achieves a smaller storage footprint, but most importantly, can perform filter operations an order of magnitude faster on low selectivity queries. Meanwhile, COMPASS maintains comparable performance on high-selectivity queries or dimension-based subsetting operations.