Time series data has a wide range of real-life applications, including financial forecasting, health monitoring, and social media analysis. However, this data often contains substantial individual privacy, and directly releasing them may lead to privacy infringement. Although existing research has proposed methods based on centralized differential privacy or local differential privacy to protect the privacy of time series data by injecting noise into the data, these methods may not be feasible in value-critical applications. Our previous work has introduced the TLDP model, which perturbs temporal data to ensure privacy guarantee while retaining original values. However, challenges persist when applying these methods to data exhibiting spatiotemporal correlations, such as trajectories. This paper proposes a novel trajectory release framework, Region-Aware Trajectory Re-sorting (RATR), which builds upon the temporal perturbation mechanism of TLDP by further introducing region-aware reachable constraints based on real road networks. In this way, it ensures privacy while significantly improving the spatial data utility. We construct region-aware reachable constraints and design a re-sorting algorithm to optimize the perturbed trajectories. Extensive experiments conducted on three real-world datasets demonstrate that our method significantly outperforms existing state-of-the-art methods in terms of data utility while maintaining a lower overall cost.

The rapid development of vector databases and large language models has significantly increased the importance of the Approximate Maximum Inner Product Search (AMIPS) problem. Over the past decade, various approaches have been proposed to efficiently address AMIPS problems. Compared to other methods, such as those based on Locality Sensitive Hashing (LSH), proximity graph-based methods have shown superior query performance on AMIPS. However, the performance of graph-based methods for AMIPS is still not fully optimized due to the norm bias issue, while graph-based methods have demonstrated strong effectiveness for ANNS. In this paper, we theoretically analyze norm bias in MIPS and identify a norm domination phenomenon: results are consistently dominated by a handful of large-norm vectors, with negligible contributions from small-norm ones. Building on this observation, we propose the Norm-Adaptive Partitioning (NAP) scheme, which splits the vectors in a dataset into H ead , B ody , and T ail based on vector norms. The H ead contains a small number of large-norm vectors for exhaustive search; the T ail includes small-norm vectors that can be safely pruned under accuracy bounds; and the B ody , characterized by concentrated norms, is well-suited for conventional proximity graphs. The main challenge of the NAP strategy is balancing query accuracy and the extra cost introduced by NAP by efficiently determining the sizes of ail , H ead , and B ody . To address this, we further design a practical NAP algorithm that minimizes search cost while ensuring bounded accuracy. To demonstrate NAP, we introduce a hybrid index combining a lightweight structure (for example, a hash table) for the head and a proximity graph for the body. Experimental results show that NAP reduces the index size of existing proximity graphs by over 50%, and the NAP-based hybrid index enables more than 2x speedup over state-of-the-art graph-based AMIPS methods at the same recall level.

Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy (LDP) was proposed as a privacy-preserving technique. Existing LDP item mining mechanisms primarily concentrate on global statistics, i.e., those from the entire dataset. Nevertheless, they fall short of user-tailored tasks such as personalized recommendations, whereas classwise statistics can improve task accuracy with fine-grained information. Meanwhile, the introduction of class labels brings new challenges. Label perturbation may result in invalid items for aggregation. To this end, we propose frameworks for multi-class item mining, along with two mechanisms: validity perturbation to reduce the impact of invalid data, and correlated perturbation to preserve the relationship between labels and items. We also apply these optimized methods to two multi-class item mining queries: frequency estimation and top-k item mining. Through theoretical analysis and extensive experiments, we verify the effectiveness and superiority of these methods.

Graph analysis has become increasingly popular with the prevalence of big data and machine learning. Traditional graph data analysis methods often assume the existence of a trusted third party to collect and store the graph data, which does not align with real-world situations. To address this, some research has proposed utilizing Local Differential Privacy (LDP) to collect graph data or graph metrics (e.g., clustering coefficient). This line of research focuses on collecting two atomic graph metrics (the adjacency bit vectors and node degrees) from each node locally under LDP to synthesize an entire graph or generate graph metrics. However, they have not considered the security issues of LDP for graphs. In this paper, we bridge the gap by demonstrating that an attacker can inject fake users into LDP protocols for graphs and design data poisoning attacks to degrade the quality of graph metrics. In particular, we present three data poisoning attacks to LDP protocols for graphs. As a proof of concept, we focus on data poisoning attacks on two classical graph metrics: degree centrality and clustering coefficient. We further design two countermeasures for these data poisoning attacks. Experimental study on real-world datasets demonstrates that our attacks can largely degrade the quality of collected graph metrics, and the proposed countermeasures cannot effectively offset the effect, which calls for the development of new defenses.

Local differential privacy (LDP) is a promising privacy model for data collection that protects sensitive information of individuals. However, applying LDP to top- $k$ estimation in set-valued data (e.g., identifying most frequent $k$ items) may yield poor results for small and sparse datasets due to high sensitivity and heavy perturbation. To address this, we propose an adaptive approach that frames the problem as a multi-armed bandit (MAB) problem, in which the decision-maker selects actions based on information collected from previous rounds to maximize the total reward over time. Inspired by this, we present two adaptive sampling schemes based on MAB: ARBS for identifying top- $k$ items and ARBSF for both top- $k$ item discovery and frequency estimation on these items. Furthermore, to address the potential long delay of multi-round collection, we propose an optimization technique to reduce the time complexity. Both theoretical and empirical results show that our adaptive sampling schemes significantly outperform existing alternatives.

Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the original values to ensure acceptable privacy, which inevitably results in value distortion and utility deterioration. In this work, we propose an alternative approach - instead of perturbing values, we apply randomization to indexes of values while ensuring rigorous LDP guarantees. Inspired by the deniability of randomized indexes, we present CRIAD for answering subset counting queries on set-value data. By integrating a multi-dummy, multi-sample, and multi-group strategy, CRIAD serves as a fully scalable solution that offers flexibility across various privacy requirements and domain sizes, and achieves more accurate query results than any existing methods. Through comprehensive theoretical analysis and extensive experimental evaluations, we validate the effectiveness of CRIAD and demonstrate its superiority over traditional value-perturbation mechanisms.

Shortest path computation is a basic operation for many graph-based applications and has been extensively studied. However, most existing works only consider the optimal path of a single criterion but ignore real-world situations involving multiple criteria. This paper investigates a new Multi-Criteria Shortest Paths (MCSPs) problem, aiming to compute the shortest paths of all criteria between a vertex pair. It is significant for real-world applications such as GPS navigation and social network analysis. Nevertheless, the rapid growth of graph size or memory-limited devices poses a memory-constraint challenge, making the adaptation of existing methods extremely time-consuming. To solve the memory-constraint MCSPs problem, we propose a general STOP & SHARE scheme to synchronize the search speeds of all criteria for sharing partition accesses. Two algorithms called OHP and MHP , adopting the one-hop strategy and partition exhaustive strategy, respectively, are proposed for implementing our scheme. Moreover, we develop two optimized algorithms, BMHP and BMHPS , to improve query efficiency by combining MHP with the bidirectional technique and a novel in-partition shortcut optimization . We also investigate partition-oriented I/O management . Experimental studies on large real-world graphs demonstrate the effectiveness of the proposed methods over the multi-pass adaptations of the existing methods.

Local differential privacy provides strong user privacy protection but is vulnerable to poisoning attacks launched by malicious users, leading to contaminative estimates. Although various works explore attacks with different manipulation targets, a practical and relatively general defense has remained elusive. In this paper, we address this problem in basic histogram estimation scenarios. We model adversaries as Byzantine users who can collaborate to maximize their attack goals. From the perspective of attackers' capability, we analyze the impact of poisoning attacks on data utility and introduce a significant threat the maximal loss attack (MLA). Considering that a high-utility-damage attack would break the smoothness of histograms, we propose the defense method, LDP-Purifier, to sterilize the poisoned histograms. Our extensive experiments validate the effectiveness of the LDP-Purifier, show-casing its ability to significantly suppress estimation errors caused by various attacks.

The maximum inner product search (MIPS) problem in high-dimensional vector spaces has various applications, primarily driven by the success of deep neural network-based embedding models. Existing MIPS methods designed for dense vectors using approximate techniques like locality-sensitive hashing (LSH) have been well studied, but they are not efficient and effective for searching sparse vectors due to the near-orthogonality among the sparse vectors. The solutions to MIPS over sparse vectors rely heavily on inverted lists, resulting in poor query efficiency, particularly when dealing with large-scale sparse datasets. In this paper, we introduce SOSIA, a novel framework specifically tailored to address these limitations. To handle sparsity, we propose the SOS transformation, which converts sparse vectors into a binary space while providing an unbiased estimator of the inner product between any two vectors. Additionally, we develop a minHash-based index to enhance query efficiency. We provide a theoretical analysis on the query quality of SOSIA and present extensive experiments on real-world sparse datasets to validate its effectiveness. The experimental results demonstrate its superior performance in terms of query efficiency and accuracy compared to existing methods.

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to collect aggregate information about a population without disclosing each user's privacy. In particular, each user locally encodes and perturbs his private data before sending it to the data collector, who aggregates and estimates the statistics about the population based on the collected perturbed values from individuals. Owing to their growing importance, LDP protocols have been widely studied and deployed in real-world scenarios (eg Chrome and Windows). However, as data poisoning attacks may be injected by attackers who introduce many fake users, the utility of the statistics is heavily poisoned. In this paper, we present a generic and extensible framework called LDPGuard to address the problem. LDPGuard provides effective defenses against data poisoning attacks to LDP protocols for frequency estimation, a basic query of most data analytics tasks. In particular, it first precisely estimates the percentage of fake users and then provides adversarial schemes to defend against particular data poisoning attacks. Experimental study on real-world and synthetic datasets demonstrates the superiority of LDPGuard compared to existing techniques.

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ω-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes.

Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VISUALNEO that facilities an efficient subgraph query in large graph databases. VISUALNEO inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.

In big data era, companies and organizations are keen to collect data from users and analyse their behaviour patterns to make decisions or predictions for profits. However, it undermines users' privacy because the collected data can be quite sensitive and easy to leak. To address privacy problems, local differential privacy (LDP) has been proposed for untrusted data collectors to obtain statistical information without compromising user privacy. Most studies on LDP assume that all users fully cooperate and contribute to the data collection process and thus the collected dataset is complete. However, in practice, especially when user population is large, such assumption seldom holds due to communication loss, user unresponsiveness or unwillingness, and incomplete user-side data. Unfortunately, state-of-the-art LDP-based data collection schemes, such as GRR, OUE and OLH, cannot handle partial data collection effectively. In this paper, we propose collaborative sampling to address partial data collection in a multi-dimensional setting. Thanks to a two-phase mechanism, we can derive the optimal sampling rate for each dimension. The optimality is shown and proved with respect to the variance of estimated frequency. Besides that, collaborative sampling is general and can be used in GRR, OUE and OLH with minimal adaption. Through experimental results, we show collaborative sampling outperforms existing mainstream data collection schemes in partial multi-dimensional data collection.

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index , which replaces or complements traditional index structures with machine learning models, has been actively explored to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces. In LIMS, the underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.

A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this article, we propose a two-phase mechanism $PrivKVM^*$ as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results.

Vehicle trajectory data is critical for traffic management and location-based services. However, the released trajectories raise serious privacy concerns because they contain sensitive information such as homes and workplaces. Based on differential privacy, this problem can be addressed by generating synthetic trajectories from the original sensitive data while guaranteeing personal privacy. Unfortunately, existing methods focus on synthesizing trajectory datasets that preserve summary-level statistics (e.g., the overall distribution of user movements), making these synthetic trajectories lose individual-level mobility patterns. As shown in our experiment, this results in the low performance of their synthetic datasets in real-world applications. To address these limitations, we propose a novel solution for Synthesizing Private and Realistic Trajectories, namely SPRT, whose key idea is to integrate the public geography structures of the target area into the process of private trajectory synthesis. This enables us to capture more accurate mobility patterns to synthesize realistic trajectories, which can preserve both summary-level statistics and individual-level mobility behaviors. Consequently, the synthetic trajectories generated by SPRT are more similar to real trajectories and therefore more practical. We evaluate the performance of SPRT in real-world applications by applying its synthetic data to a series of trajectory analytic tasks. The results demonstrate that our solution improves data utility by at least 37% over state-of-the-art approaches.

Computing the shortest path distance between two vertices in a road network is a building block in numerous applications. To do so efficiently, the state-of-the-art proposals adopt a tree decomposition process with heuristic strategies to build 2-hop label indexes. However, these indexes suffer from large space overheads caused by either tree imbalance or a large tree height. Independently of this, reinforcement learning has recently show impressive performance at sequential decision making in spatial data management tasks. We observe that tree decomposition is naturally a sequential decision making problem that decides which vertex to process at each step. In this paper, we propose a reinforcement learning based tree decomposition (RLTD) approach that reduces the space overhead significantly. We model tree decomposition as a Markov Decision Process, exploiting features of both the network topological structure and the tree structure. We further optimize the tree decomposition process by taking the network density into account, which yields a great generalization of the model on large road networks. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they are able to reduce the space overhead by about 51% and achieve on average about 14% speedup for queries with almost the same preprocessing time when compared with the state-of-the-art proposals.

Designing a space-efficient data structure to answer membership queries while ensuring high accuracy and real-time response is a challenging task in the field of stream processing. Many techniques have been developed to answer these queries in a sliding windows manner. However, assuming the user will conduct the query with the presupposed window size is not always practical. In this paper, we introduce a novel data structure called Learned Cuckoo Filter (LCF). It can provide satisfactory results for the approximate membership query on data streams, regardless of the user-defined query windows. LCF operates by adaptively maintaining cuckoo filters with the assistance of a well-trained oracle that learned the frequency feature of the data within the stream. To further enhance memory utilization, we develop a compact version of LCF (denoted by LCF_C), which selectively removes redundant information to reduce space consumption without compromising query accuracy. Furthermore, we conduct a thorough theoretical analysis of query accuracy and provide detailed guidelines for optimal parameter selection (denoted by LCF_O). Extensive experimental studies on synthetic and real-world datasets demonstrate the superiority of the proposed methods in terms of both space consumption and accuracy. Compared to the state-of-the-art algorithms, LCF_O can reduce up to 61% of space cost at the same error level, and achieve up to 12× improved accuracy with the same space cost.

The approximate nearest neighbor (ANN) search in high-dimensional spaces is a fundamental but computationally very expensive problem. Many methods have been designed for solving the ANN problem, such as LSH-based methods and graph-based methods. The LSH-based methods can be costly to reach high query quality due to the hash-boundary issues, while the graph-based methods can achieve better query performance by greedy expansion in an approximate proximity graph (APG). However, the construction cost of these APGs can be one or two orders of magnitude higher than that for building hash-based indexes. In addition, they fail short in incrementally maintaining APGs as the underlying dataset evolves. In this paper, we propose a novel approach named LSH-APG to build APGs and facilitate fast ANN search using a lightweight LSH framework. LSH-APG builds an APG via consecutively inserting points based on their nearest neighbor relationship with an efficient and accurate LSH-based search strategy. A high-quality entry point selection technique and an LSH-based pruning condition are developed to accelerate index construction and query processing by reducing the number of points to be accessed during the search. LSH-APG supports fast maintenance of APGs in lieu of building them from scratch as dataset evolves. Its maintenance cost and query cost for a point is proven to be less affected by dataset cardinality. Extensive experiments on real-world and synthetic datasets demonstrate that LSH-APG incurs significantly less construction cost but achieves better query performance than existing graph-based methods.