With the prevalence of data-driven business, data markets where data can be commoditized, circulated, and ex-ploited are gaining considerable interest in the data management community. However, the uncertainty in data value poses great challenges for data pricing and thus data trading, which is magnified by the externality arising from the replicable nature of data. In this paper, we present the first bargaining-based data market framework to resolve the externality in data markets. Gearing toward raw data trading, we propose a three-stage bar-gaining model to formulate trading dynamics, which ascertains the data price agreed by both sellers and buyers. With parameters instantiated in preparation stage, an iterative bidding algorithm with provable convergence is designed in negotiation stage to solve the data pricing problem by eliciting equilibrium bids from participants with their profits optimized. Approximation algorithms with guaranteed bounds are presented in settlement stage to solve the NP-hard data allocation problem for profit maximization for the data seller with individual rationality satisfied for data buyers. Experiments on real datasets verify the effectiveness and efficiency of our framework.

Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML).

Continuous release of data streams is frequently used in numerous applications. However, when data is sensitive, this poses privacy risks. To mitigate this risk, efforts have been devoted to devising techniques that satisfy a formal privacy notion called w-event differential privacy. Nevertheless, a recent benchmark reveals that none of the existing works offer a universally effective solution across all types of data streams, making it challenging to select an appropriate scheme for unknown data streams in practical scenarios. We identify that all existing methods are heuristic-based and make data-independent decisions. In this paper, we change this landscape by introducing SPAS which is built on data-dependent strategies. Specifically, SPAS continuously predicts an optimal publishing strategy within each sliding window that minimizes the error of the released results based on the characteristics of the data stream. Additionally, we develop a weighted sparse vector technique to control data sampling and manage privacy budget consumption following that optimal publishing strategy. Comprehensive experimental evaluations demonstrate the efficacy of SPAS in adapting to diverse one-dimensional and multi-dimensional data streams for both data release and range query tasks. Our code is open-sourced.

The explosive growth of data has highlighted its critical role in driving economic growth through data marketplaces, which enable extensive data sharing and access to high-quality datasets. To support effective trading, signaling mechanisms provide participants with information about data products before transactions, enabling informed decisions and facilitating trading. However, due to the inherent free-duplication nature of data, commonly practiced signaling methods face a dilemma between privacy and reliability, undermining the effectiveness of signals in guiding decision-making. To address this, this paper explores the benefits and develops a non-TCP-based construction for a desirable signaling mechanism that simultaneously ensures privacy and reliability. We begin by formally defining the desirable utility signaling mechanism and proving its ability to prevent suboptimal decisions for both participants and facilitate informed data trading. To design a protocol to realize its functionality, we propose leveraging maliciously secure multi-party computation (MPC) to ensure the privacy and robustness of signal computation and introduce an MPC-based hash verification scheme to ensure input reliability. In multi-seller scenarios requiring fair data valuation, we further explore the design and optimization of the MPC-based KNN-Shapley method with improved efficiency. Rigorous experiments demonstrate the efficiency and practicality of our approach.

Semantic caching significantly reduces computational costs and improves efficiency by storing and reusing large language model (LLM) responses. However, existing systems rely primarily on matching individual queries, lacking awareness of multi-turn dialogue contexts, which leads to incorrect cache hits when similar queries appear in different conversational settings. This demonstration introduces ContextCache, a context-aware semantic caching system for multi-turn dialogues. ContextCache employs a two-stage retrieval architecture that first executes vector-based retrieval on the current query to identify potential matches and then integrates current and historical dialogue representations through self-attention mechanisms for precise contextual matching. Evaluation of real-world conversations shows that ContextCache improves precision and recall compared to existing methods. Additionally, cached responses exhibit approximately 10 times lower latency than direct LLM invocation, enabling significant computational cost reductions for LLM conversational applications.

Data valuation is a core function in data markets and cooperative data sharing.Shapley valueis a widely used approach to fairly measure the contribution of data points towards a collective utility (e.g., a machine learning model trained from the data). However, computing Shapley values is known to be in general #P-hard due to the exponential utility evaluation. Furthermore, the presence of dynamic data poses additional challenges due to the prohibitively expensive cost of recomputing from scratch. In this paper, we study the problem ofDynamic Shapley Value Computation, which focuses on updating Shapley values when dynamically adding or deleting data points. For adding, to prune redundant computation of overlapping model utilities, we propose the pivot-based algorithm that can reduce half the computation time in expectation. We also propose delta-based algorithms to capture Shapley value changes, which require only a smaller sample size to converge. For deleting, we present the YN-NN algorithm that derives the new Shapley values from precomputed utilities efficiently. Based on Shapley value changes, we give another version of the delta-based algorithm for deleting data points. Besides, we propose heuristic algorithms that draw on experimental observations for addition, deletion, and hybrid scenarios. Extensive experimental results demonstrate the efficiency and effectiveness of our proposed algorithms.

With the prevalence of data-driven intelligence, data markets with various data products are gaining considerable interest as a promising paradigm for commoditizing data and facilitating data flow. In this paper, we present Stackelberg-Nash based Data Markets (Share) to first realize a demand-driven incentivized data market with absolute pricing. We propose a three-stage Stackelberg-Nash game to model trading dynamics which not only optimizes the profits of all selfish participants but also adapts to the common buyer-broker-sellers market flow and solves the seller selection problem based on sellers' inner competition. We define Stackelberg-Nash Equilibrium and use backward induction to solve the equilibrium. For inner Nash equilibrium, we apply the conventional direct derivation approach and propose a novel mean-field based method along with provable approximation guarantees for complicated cases where direct derivation fails. Experiments on real datasets verify the effectiveness and efficiency of Share.

Driven by the growing field of data intelligence, data market emerges as a promising paradigm for data exchange, enabling the full utilization of data. Data pricing is an essential function in data market that reflects the values or cost of data and is dependent on interactions among multiple market participants including data buyers, data sellers, and data brokers. Game theory presents a promising approach to model the multi-participant interplay in data pricing, yet challenged by the specific nature of data. In this paper, we present a blueprint for applying game theory to data pricing. From a game-theoretic perspective, we highlight the unique characteristics of data (compared to traditional goods) and suggest important desiderata for effective data pricing. We identify four key dimensions (Participant, Object, Action, and Information) to understand the landscape of game theory based data pricing. Within each dimension, data-specific challenges and research gaps are identified. Our work establishes a foundational understanding of data pricing through the lens of game theory and opens up promising research directions in this developing field.

The popularity of cloud computing has made outsourced databases prevalent in real-world applications. To protect data security, numerous encrypted outsourced databases have been proposed for this paradigm. However, the maintenance of encrypted databases (EDBs) has scarcely been addressed. In this paper, we focus on a typical maintenance task - functional dependency (FD) discovery. We develop novel FD protocols in EDBs while guaranteeing minimal leakages: nothing is revealed besides the database size and the actual discovered FDs. As far as we know, we are the first to formally define secure FD discovery with minimal leakage. We present two oblivious FD discovery protocols and prove them secure in the presence of the persistent adversary (monitoring processes on the server). The first protocol leverages oblivious RAM (ORAM) and is suitable for dynamic databases. The second protocol relies on oblivious sorting and is more practical in static databases due to high parallelism. We present a thorough experimental evaluation of the proposed methods.

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy. In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.

Oblivious map (OMAP) is an important component in encrypted databases, utilized to prevent the server inferring sensitive information about client's encrypted databases based on access patterns. Despite its widespread usage and importance, existing OMAP solutions face practical challenges, including the need for a large number of interaction rounds between the client and server, as well as substantial communication bandwidth. For example, the SOTA protocol OMIX++ in VLDB 2024 still requires O (log n ) interaction rounds and O (log 2 n ) communication bandwidth per access, where n denotes the total number of key-value pairs stored. In this work, we introduce more practical and efficient OMAP constructions. Consistent with all prior OMAPs, our constructions also adapt only the tree-based Oblivious RAM (ORAM) and oblivious data structures (ODS) to achieve OMAP for enhanced practicality. In complexity, our approach needs O (log n /log log n )+ O (log λ ) interaction rounds and O (log 2 n /log log n ) + O (log λ log n ) communication bandwidth per data access where λ is the security parameter. This new complexity results from our two main contributions. First, unlike prior works relying solely on search trees , we design a novel framework for OMAP that combines hash table with search trees. Second, we propose a more efficient tree-based ORAM named DAORAM, which is of significant independent interest. This new ORAM accelerates our constructions as it supports obliviously accessing hash tables more efficiently. We implement both our proposed constructions and prior methods to experimentally demonstrate that our constructions substantially outperform prior methods in terms of efficiency.

The Shapley value provides a unique approach to equitably gauge each player's contribution within a coalition and has extensive applications with various utility functions. In data valuation for machine learning, particularly for classification tasks, using classification accuracy as the utility function has become a de facto standard. However, accuracy can be an imprecise metric, potentially missing finer details crucial for valuation. In this paper, we propose the probability-based Shapley (P-Shapley) value, which leverages predicted probabilities to heighten utility differentiation. Several convex calibration functions are further incorporated for probability calibration. We prove that the P-Shapley value outperforms Shapley values based on accuracy or other coarse metrics in approximation stability and the discrimination of marginal utility change can be further improved by convex calibration functions. Extensive experiments on four real-world datasets demonstrate the effectiveness of our approaches.

Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitigate these leakages, they either (1) incur substantial overheads, (2) focus on specific subsets of leakage patterns, or (3) apply the same security notion across various workloads, thereby impeding the attainment of fine-tuned privacy-efficiency trade-offs. In light of various detrimental leakage patterns, this paper starts with an investigation into which specific leakage patterns require our focus in the contexts of key-value, range-query, and dynamic workloads, respectively. Subsequently, we introduce new security notions tailored to the specific privacy requirements of these workloads. Accordingly, we propose and instantiate Swat, an efficient construction that progressively enables these workloads, while provably mitigating system-wide leakage via a suite of algorithms with tunable privacy-efficiency trade-offs. We conducted extensive experiments and compiled a detailed result analysis, showing the efficiency of our solution. Swat is about an order of magnitude slower than an encryption-only data store that reveals various leakage patterns and is two orders of magnitude faster than a trivial zero-leakage solution. Meanwhile, the performance of Swat remains highly competitive compared to other designs that mitigate specific types of leakage.

With the flourishing of data-driven applications, data marketplaces, which can dramatically facilitate data utilization, have emerged recently. However, determining the appropriate price for datasets presents a significant challenge due to the intangible nature of data. DataPrice alleviates the challenge by providing an interactive system based on a pricing model trained on real datasets collected from commercial data marketplaces. The pricing model can estimate an appropriate price according to the dataset description as a reference for both data sellers and data buyers. By leveraging Shapley values to evaluate the contribution of each attribute in metadata on the estimated price, DataPrice offers an explanation of the pricing process in a text form to enhance user trust.

Shapley value provides a unique way to fairly assess each player's contribution in a coalition and has enjoyed many applications. However, the exact computation of Shapley value is #P-hard due to the combinatoric nature of Shapley value. Many existing applications of Shapley value are based on Monte-Carlo approximation, which requires a large number of samples and the assessment of utility on many coalitions to reach high-quality approximation, and thus is still far from being efficient. Can we achieve an efficient approximation of Shapley value by smartly obtaining samples? In this paper, we treat the sampling approach to Shapley value approximation as a stratified sampling problem. Our main technical contributions are a novel stratification design and a sampling method based on Neyman allocation. Moreover, computing the Shapley value in a dynamic setting, where new players may join the game and others may leave it poses an additional challenge due to the considerable cost of recomputing from scratch. To tackle this issue, we propose to capture changes in Shapley value, making our approaches applicable to scenarios with dynamic players. Experimental results on several real data sets and synthetic data sets demonstrate the effectiveness and efficiency of our approaches.

With the prevalence of data-driven research, data valuation has attracted attention from the computer science field. How to appraise a single datum becomes an imperative problem, especially in the context of machine learning. Shapley value is widely used to fairly measure the contribution of data points in machine learning since it is the unique definition that satisfies all four desired properties: balance, symmetry, additivity, and zero element. However, computing Shapley value is known to be a #P-hard problem. As data is subject to changes, dynamic data exists pervasively in real-world scenarios. Pricing such dynamic data is more challenging due to the prohibitively expensive cost of recalculation from scratch. In this paper, we study the problem of Dynamic Shapley Value Computation, which updates Shapley value when dynamically adding/deleting data points. For adding data points, to prune unnecessary computation of overlapping model utilities, we propose the pivot-based algorithm that can reduce half computation time in general. We also propose the delta-based algorithm to capture Shapley value changes, which requires a smaller sample size to converge. For deleting data points, we present the YN-NN algorithm that derives the new Shapley value from the data structure of precomputed model utilities in an efficient way. Based on Shapley value changes, we give another version of the delta-based algorithm for deleting data points. Besides, we propose heuristic algorithms to draw on experimental observations for both adding and deleting data points. Extensive experimental results demonstrate the efficiency and effectiveness of our proposed algorithms.

Functional dependencies (FDs) have been extensively employed in discovering inferential relationships in databases, which provide feasible approaches for many data mining tasks, such as data obfuscation, query optimization, and schema normalization. Since the explosive growth of data leads to a rapid increase of FDs on large datasets, existing algorithms that pay more attention to the exact FD discovery cannot extract FDs efficiently. To bridge this gap, we propose an Efficient double-cycle approximation of Functional Dependency (EulerFD) discovery algorithm, which ensures both efficiency and accuracy of FD discovery. EulerFD induces FDs from invalid ones as invalidating an FD only requires comparing and verifying some pairs of tuples (that violate the dependency) while validating an FD requires examining and verifying all tuples. Considering the abundant tuple pairs in large datasets, a novel sampling strategy is employed in EulerFD to quickly extract invalid FDs by revising the sampling range according to previous sampling results. Furthermore, EulerFD evaluates the stopping criteria in a double-cycle structure as feedback for further sampling. The sampling strategy and the double-cycle structure complement each other to achieve a more efficient sampling effect. Experimental results on real-world and synthetic datasets, especially the massive datasets from DMS of Alibaba Cloud, justify the design and verify the efficiency and effectiveness of the proposed EulerFD.

Federated Learning (FL) allows clients to form a consortium to train a global model under the orchestration of a central server while keeping data on the local client without sharing it, thus mitigating data privacy issues. However, training a robust global model is challenging since the local data is invisible to the server. The local data of clients are naturally heterogeneous, while some clients can use corrupted data or send malicious updates to interfere with the training process artificially. Meanwhile, communication and computation costs are inevitable challenges in designing a practical FL algorithm. In this paper, to improve the robustness of FL, we propose a Shapley value-inspired adaptive weighting mechanism, which regards the FL training as sequential cooperative games and adjusts clients' weights according to their contributions. We also develop a client sampling strategy based on importance sampling, which can reduce the communication cost by optimizing the variance of the global updates according to the weights of clients. Furthermore, to diminish the computation cost of the server, we propose a weight calculation method by estimating differences between the Shapley value of clients. Our experimental results on several real data sets demonstrate the effectiveness of our approaches.

Shapley value provides a unique way to fairly assess each player's contribution in a coalition and has enjoyed many applications. However, the exact computation of Shapley value is #P-hard due to the combinatoric nature of Shapley value. Many existing applications of Shapley value are based on Monte-Carlo approximation, which requires a large number of samples and the assessment of utility on many coalitions to reach high quality approximation, and thus is still far from being efficient. Can we achieve an efficient approximation of Shapley value by smartly obtaining samples? In this paper, we treat the sampling approach to Shapley value approximation as a stratified sampling problem. Our main technical contributions are a novel stratification design and two sample allocation methods based on Neyman allocation and empirical Bernstein bound, respectively. Experimental results on several real data sets and synthetic data sets demonstrate the effectiveness and efficiency of our novel stratification design and sampling approaches.

Order-preserving encryption (OPE) allows efficient comparison operations over encrypted data and thus is popular in encrypted databases. However, most existing OPE schemes are vulnerable to inference attacks as they leak plaintext frequency. To this end, some frequency-hiding order-preserving encryption (FH-OPE) schemes are proposed and claim to prevent the leakage of frequency. FH-OPE schemes are considered an important step towards mitigating inference attacks. Unfortunately, there are still vulnerabilities in all existing FH-OPE schemes. In this work, we revisit the security of all existing FH-OPE schemes. We are the first to demonstrate that plaintext frequency hidden by them is recoverable. We present three ciphertext-only attacks named frequency-revealing attacks to recover plaintext frequency. We evaluate our attacks in three real-world datasets. They recover over 90% of plaintext frequency hidden by any existing FH-OPE scheme. With frequency revealed, we also show the potentiality to apply inference attacks on existing FH-OPE schemes. Our findings highlight the limitations of current FH-OPE schemes. Our attacks demonstrate that achieving frequency-hiding requires addressing the leakages of both non-uniform ciphertext distribution and insertion orders of ciphertexts, even though the leakage of insertion orders is always ignored in OPE.

The increasing demand for data-driven machine learning (ML) models has led to the emergence of model markets, where a broker collects personal data from data owners to produce high-usability ML models. To incentivize data owners to share their data, the broker needs to price data appropriately while protecting their privacy. Forequitable data valuation, which is crucial in data pricing,Shapley valuehas become the most prevalent technique because it satisfies all four desirable properties in fairness: balance, symmetry, zero element, and additivity. Forthe right to be forgotten, which is stipulated by many data privacy protection laws to allow data owners to unlearn their data from trained models, thesharded structurein ML model training has become a de facto standard to reduce the cost of future unlearning by avoiding retraining the entire model from scratch. In this paper, we explore how the sharded structure for the right to be forgotten affects Shapley value for equitable data valuation in model markets. To adapt Shapley value for the sharded structure, we propose S-Shapley value, a sharded structure-based Shapley value, which satisfies four desirable properties for data valuation. Since we prove that computing S-Shapley value is #P-complete, two sampling-based methods are developed to approximate S-Shapley value. Furthermore, to efficiently update valuation results after data owners unlearn their data, we present two delta-based algorithms that estimate the change of data value instead of the data value itself. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.

Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source.

One reason for the popularity of Bitcoin is due to its anonymity. Although several heuristics have been used to break the anonymity, new approaches are proposed to enhance its anonymity at the same time. One of them is the mixing service. Unfortunately, mixing services have been abused to facilitate criminal activities, e.g., money laundering. As such, there is an urgent need to systematically understand Bitcoin mixing services.

Encrypted database is an innovative technology proposed to solve the data confidentiality issue in cloud-based DB systems. It allows a data owner to encrypt its database before uploading it to the service provider; and it allows the service provider to execute SQL queries over the encrypted data. Most of existing encrypted databases (e.g., CryptDB in SOSP '11) do not support data interoperability: unable to process complex queries that require piping the output of one operation to another. To the best of our knowledge, SDB (SIGMOD '14) is the only encrypted database that achieves data interoperability. Unfortunately, we found SDB is not secure! In this paper, we revisit the security of SDB and propose a ciphertext-only attack named co-prime attack. It successfully attacks the common operations supported by SDB, including addition, comparison, sum, equi-join and group-by. We evaluate our attack in three real-world benchmarks. For columns that support addition and comparison , we recover 84.9% -- 99.9% plaintexts. For columns that support sum, equi-join and group-by , we recover 100% plaintexts. Besides, we provide potential countermeasures that can prevent the attacks against sum, equi-join, group-by and addition. It is still an open problem to prevent the attack against comparison.

Data-driven machine learning (ML) has witnessed great success across a variety of application domains. Since ML model training relies on a large amount of data, there is a growing demand for high-quality data to be collected for ML model training. Data markets can be employed to significantly facilitate data collection. In this work, we demonstrate Dealer, an en D -to-end model m a rketp l ace with diff e rential p r ivacy. Dealer consists of three entities, data owners, the broker, and model buyers. Data owners receive compensation for their data usages allocated by the broker; The broker collects data from data owners, builds and sells models to model buyers; Model buyers buy their target models from the broker. We demonstrate the functionalities of the three participating entities and the abbreviated interactions between them. The demonstration allows the audience to understand and experience interactively the process of model trading. The audience can act as a data owner to control what and how the data would be compensated, can act as a broker to price machine learning models with maximum revenue, as well as can act as a model buyer to purchase target models that meet expectations.

Soliciting answers from online users is an efficient and effective solution to many challenging tasks. Due to the variety in the quality of users, it is important to infer their ability to provide correct answers during aggregation. Therefore, truth discovery methods can be used to automatically capture the user quality and aggregate user-contributed answers via a weighted combination. Despite the fact that truth discovery is an effective tool for answer aggregation, existing work falls short of the protection towards the privacy of participating users. To fill this gap, we propose perturbation-based mechanisms that provide users with privacy guarantees and maintain the accuracy of aggregated answers. We first present a one-layer mechanism, in which all the users adopt the same probability to perturb their answers. Aggregation is then conducted on perturbed answers but the aggregation accuracy could drop accordingly. To improve the utility, a two-layer mechanism is proposed where users are allowed to sample their own probabilities from a hyper distribution. We theoretically compare the one-layer and two-layer mechanisms, and prove that they provide the same privacy guarantee while the two-layer mechanism delivers better utility. This advantage is brought by the fact that the two-layer mechanism can utilize the estimated user quality information from truth discovery to reduce the accuracy loss caused by perturbation, which is confirmed by experimental results on real-world datasets. Experimental results also demonstrate the effectiveness of the proposed two-layer mechanism in privacy protection with tolerable accuracy loss in aggregation.