Model inference systems are essential for implementing end-to-end data analytics pipelines that deliver the benefits of machine learning models to users. Existing cloud-based model inference systems are costly, not easy to scale, and must be trusted in handling the models and user request data. Serverless computing presents a new opportunity, as it provides elasticity and fine-grained npricing. Our goal is to design a serverless model inference system that protects models and user request data from untrusted cloud providers. It offers high performance and low cost, while requiring no intrusive changes to the current serverless platforms. To realize our goal, we leverage trusted hardware. We identify and address three challenges in using trusted hardware for serverless model inference. These challenges arise from the high-level abstraction of serverless computing, the performance overhead of trusted hardware, and the characteristics of model inference workloads. We present SeSeMI, a secure, efficient, and cost-effective serverless model inference system. It adds three novel features non-intrusively to the existing serverless infrastructure and nothing else. The first feature is a key service that establishes secure channels between the user and the serverless instances, which also provides access control to models and users' data. The second is an enclave runtime that allows one enclave to process multiple concurrent requests. The final feature is a model packer that allows multiple models to be executed by one serverless instance. We build SeSeMI on top of Apache Open Whisk, and conduct extensive experiments with three popular machine learning models. The results show that SeSeMI achieves low latency and low cost at scale for realistic workloads.

Subspace-based DPSGD has emerged as a robust solution for alleviating excessive noise in high-dimensional, privacy-preserving visual learning. It achieves a favorable privacy-utility balance by projecting privatized gradients onto a low-rank subspace derived from in-distribution public data. However, relying solely on limited public data can narrow the diversity of the anchored subspace and induce over-memorization into model training, leading to suboptimal private optimization.To overcome these limitations, we propose a synthesis-augmented subspace-based DPSGD framework, SAS-DPSGD, which integrates synthetic data into the subspace construction process. We quantitatively analyze the private optimization performance using the subspace derived from the mixed public and synthetic data, revealing the benefits as well as the saturation effects of incorporating synthetic data in private visual learning. To the best of our knowledge, this is the first work to provide a unified theoretical guarantee to synthesis-augmented subspace-based DPSGD. Moreover, we design an early projection mechanism within our framework that projects the gradient onto the subspace before performing gradient clipping. This mechanism effectively reduces the gradient clipping bias and lowers the synthetic data requirement, resulting in a faster convergence rate. Extensive experiments on two real-world datasets validate that SAS-DPSGD outperforms nine baselines by up to 9.78% in accuracy and can reduce the amount of synthetic data required by 66.7%.

Federated learning (FL) is an emerging paradigm that enables multiple clients to collaboratively train a machine learning (ML) model without the need to exchange their raw data. However, it relies on a centralized authority to coordinate participants’ activities. This not only interrupts the entire training task in case of a single point of failure, but also lacks an effective regulatory mechanism to prevent malicious behavior. Although blockchain, with its decentralized architecture and data immutability, has significantly advanced the development of FL, it still struggles to withstand poisoning attacks and faces limitations in computational scalability. We propose Zkfhed, a verifiable and scalable FL system that overcomes the limitations of blockchain-based FL in poison attacks and computational scalability. First, we propose a two-stage audit scheme based on zero-knowledge proofs (ZKPs), which verifies that the training data are extracted from trusted organizations and that computations on the data exactly follow the specified training protocols. Second, we propose a homomorphic encryption delegation learning (HEDL), based on fully homomorphic encryption (FHE). It is capable of outsourcing complex computing to external computing resources without sacrificing the client's data privacy. Final, extensive experiments on real-world datasets demonstrate that Zkfhed can effectively identify malicious clients and is highly efficient and scalable in terms of online time and communication efficiency.

Data management is indispensable for informed decision-making in the big data era. In the meantime, Large Language Models (LLMs), equipped with billions of model parameters and trained on extensive data corpora, have recently achieved record-breaking results in various real-world applications, such as machine translation, content generation, information retrieval, etc. The emergent abilities of LLMs, e.g., in-context learning and advanced reasoning ability, have great potential to revolutionize data management. In this paper, we first present some promising categories of data management applications where LLMs can be adapted, including data generation, data transformation, data integration, and data exploration. We then discuss the corresponding challenges for such adaption. Finally, we envision potential solutions to these challenges.

Caching is a crucial solution to alleviate the high latency and low bandwidth of cloud databases. However, existing caching algorithms are not suitable for cloud databases as 1) they cannot ensure the adaptability to changing workloads; 2) they are not designed with awareness of data fetching costs. Combining learning-based models with cost-aware caching algorithms is natural for better performance. However, it is challenging due to the absence of the oracle algorithm for guiding the learning model. Moreover, current learning models incur significant computation overheads, potentially worsening the performance of cloud databases. In this paper, we propose a learning-based cost-aware caching framework called LBSC for cloud databases, ensuring faster query execution and robust performance in dynamic workloads. We first introduce an approximately optimal oracle algorithm called BeladySizeCost, which retains data items with high cost per byte that are likely to be accessed in near future. Then, we present a lightweight supervised learning-based model that learns from BeladySizeCost to predict the eviction probability of the cached data. Moreover, we design effective optimizations to reduce the computation overheads of the learning-based algorithm. Extensive experiments in both simulations and real-world cloud databases demonstrate that the proposed framework significantly outperforms the state-of-the-art baselines.

Federated learning (FL) is a popular distributed machine learning (ML) framework in which multiple parties share their model parameters instead of the raw training datasets to construct a global model in a privacy-preserving manner. However, existing FL solutions mainly focus on protecting the privacy of individual training records by incorporating differential privacy (DP), while overlooking the protection of the distribution information of training datasets, despite the fact that data distribution is also regarded as highly sensitive in high-stakes applications.In this paper, we propose the first privacy-preserving stochastic gradient descent (SGD) algorithm for protecting label distribution in FL. To establish a formal privacy guarantee, we formalize a privacy notion, dubbed (m,γ,ξ)-label distributional privacy, to quantify label distributional privacy leakage. Subsequently, we design the label distribution perturbation mechanism (LDPM) that carefully incorporates randomness into the SGD algorithm to achieve (m,γ,ξ)-label distributional privacy for all one-vs-all classification models. LDPM is easy to implement and provides non-trivial privacy guarantees, making it a suitable drop-in replacement for existing FL local model training algorithms. Notably, we demonstrate that LDPM also ensures DP, indicating that LDPM offers both individual and label distributional privacy guarantees. Extensive experiments on six benchmark datasets validate the effectiveness of LDPM.

With the development of deep learning, people are more and more concerned about the security of data. Federated learning can solve the problem of data island, but it also brings more serious data privacy problems. Furthermore, in the process of multisource data collaboration, the efficiency of the whole federated learning system is usually not high. In this article, we introduce a scheme named FVFL, which ensure the local data security and resistance to collusive attacks, more importantly it can well support client flexible participate federated learning. We adopt Paillier encryption and secret sharing to guarantee client’s data security and resistance to collusive attacks. Moreover, our encryption mechanism allows client to participate in federated learning flexibly, and the correctness of the encryption algorithm is not affected by client’s drop out. The super-increasing sequence is introduced to reduce the communication overhead of the whole system, the simulation result shows that the result is significant; the Lagrange interpolation polynomial and secret Sharing is introduced to implement verification mechanism, to prevent malicious forgery of aggregation results in the cloud. The verification mechanism ensures the clients to obtain real and reliable aggregation results in the cloud. Moreover, our verification mechanism allows client to participate in federated learning flexibly, and the correctness of the verification algorithm is not affected by client’s drop out. And the experimental results show that FVFL has high accuracy and efficiency.

Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract specific subdatasets from the database using conventional database operations, and then apply deep neural networks (DNN) training and inference on these subdatasets in a separate analytics system. The process can be prohibitively expensive, especially when there are various subdatasets extracted for different analytical purposes. This calls for efficient in-database support of advanced analytical methods. In this paper, we introduce LEADS, a novel SQL-aware dynamic model slicing technique to customize models for specified SQL queries. LEADS improves the predictive modeling of structured data via the mixture of experts (MoE) and maintains efficiency by a SQL-aware gating network. At the core of LEADS is the construction of a general model with multiple expert sub-models trained over the database. The MoE scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating necessary experts via the SQL-aware gating network during inference. To support in-database analytics, we build an inference extension that integrates LEADS onto PostgreSQL. Our extensive experiments on real-world datasets demonstrate that LEADS consistently outperforms the baseline models, and the in-database inference extension delivers a considerable reduction in inference latency compared to traditional solutions.

Federated Learning (FL) emerges as a viable solution to facilitate data collaboration, enabling multiple clients to collaboratively train a machine learning (ML) model under the supervision of a central server while ensuring the confidentiality of their raw data. However, existing studies have unveiled two main risks: (i) the potential for the server to infer sensitive information from the client's uploaded updates (i.e., model gradients), compromising client input privacy, and (ii) the risk of malicious clients uploading malformed updates to poison the FL model, compromising input integrity. Recent works utilize secure aggregation with zero-knowledge proofs (ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer from extremely low efficiency and, thus, are impractical for real deployment. In this paper, we propose a novel and highly efficient approach RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously. Firstly, we devise a probabilistic integrity check method that transforms strict checks into a hypothesis test problem, offering great optimization opportunities. Secondly, we introduce a hybrid commitment scheme to satisfy Byzantine robustness with improved performance. Thirdly, we present an optimized ZKP generation and verification technique that significantly reduces the ZKP cost based on probabilistic integrity checks. Furthermore, we theoretically prove the security guarantee of RiseFL and provide a cost analysis compared to state-of-the-art baselines. Extensive experiments on synthetic and real-world datasets suggest that our approach is effective and highly efficient in both client computation and communication. For instance, RiseFL is up to 28x, 53x, and 164x faster than baselines ACORN, RoFL, and EIFFeL for the client computation.

Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing privacy protection by avoiding sharing private data directly. However, the potential risks associated with such an approach have not been comprehensively examined. In this paper, we take an adversarial perspective to investigate the potential privacy and fairness risks associated with the sharing of diffusion models. Specifically, we investigate the circumstances in which one party (the sharer) trains a diffusion model using private data and provides another party (the receiver) black-box access to the pre-trained model for downstream tasks. We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver’s downstream models by manipulating the training data distribution of the diffusion model. Meanwhile, the receiver can perform property inference attacks to reveal the distribution of sensitive features in the sharer’s dataset. Our experiments conducted on real-world datasets demonstrate remarkable attack performance on different types of diffusion models, which highlights the critical importance of robust data auditing and privacy protection protocols in pertinent applications.

Federated learning (FL) has emerged as a promising privacy-preserving machine learning paradigm, enabling data owners to collaboratively train a joint model by sharing model parameters instead of private training data. However, recent studies reveal the privacy risks in FL by inferring private training data from model parameters. Therefore, differential privacy (DP) is incorporated into FL to safeguard training data. Nevertheless, DP does not provide a strong theoretical guarantee for protecting data distribution, which is also highly sensitive in thecross-siloFL scenarios as it may reflect the business secrets of data owners. In this paper, we develop two attack methods to investigate the potential risks of data distribution leakage in differentially private cross-silo FL. We highlight that an honest-but-curious server can successfully infer both the feature and label distributions of each party's training data without any background knowledge. Specifically, the first attack applies when models are differentiable, while the second attack caters to non-differentiable classification models. Extensive experiments on six benchmark datasets validate the effectiveness of the proposed attacks. The results demonstrate that the state-of-the-art DP-SGD algorithm is still vulnerable to the inference attack on data distribution, emphasizing the necessity of designing more advanced privacy-preserving FL frameworks.

Vertical federated learning (VFL) is an emerging paradigm for cross-silo organizations to build more accurate machine learning (ML) models. In this setting, multiple organizations (i.e., parties) hold the same set of samples with different features. However, different parties may have redundant or highly correlated features, leading to inefficient and ineffective VFL model training. Effective feature selection in VFL is therefore essential to mitigate such a problem and improve model effectiveness, as well as computation and communication efficiency. To this end, in this paper, we propose a federated feature selection framework, called FEAST, which leverages conditional mutual information (CMI) to select more informative features while having low redundancy. Furthermore, we design a communication-efficient method to reduce the information exchanged among the parties while protecting the parties' raw data. Extensive experiments on four real-world datasets demonstrate that the proposed framework achieves state-of-the-art performance in terms of accuracy, communication and computation costs.

Data collaboration enables multiple parties to pool data for deriving meaningful data insights. However, data misuse and unlawful data collection have led to precautionary measures being imposed by individual organizations to guide against data leakage and abuse. As a response, decentralized federated learning (DFL) has emerged as an attractive paradigm to facilitate data collaboration while being amenable to privacy-preserving data and knowledge sharing, cost reduction, and prediction accuracy improvement. Unfortunately, the participating parties in DFL tend to be heterogeneous with skew datasets and uneven capabilities. Inevitably, training and transmission costs, and the presence of free-riders pose challenges to the adoption and participation of DFL. The absence of centralized parameter servers further exacerbates the problem of evaluating the contribution of each individual party. Therefore, an effective incentive mechanism is essential to promote data collaboration. In this paper, we propose a novel Incentive-aware Decentralized fEderated leArning (IDEA) framework for facilitating data collaboration. Specifically, we first design a customizable reward scheme for heterogeneous parties to optimize their respective objectives such as higher model accuracy, communication efficiency, and computational efficiency. To reward fairly to deserving parties while offering flexibility, we propose a novel multi-agent reinforcement learning (MARL) incentive mechanism, which enables heterogeneous parties to learn their own optimal collaboration policy. We then design an efficient decentralized data collaboration algorithm that supports the customizable reward scheme based on individual objective-specific collaboration policy. We theoretically prove that the algorithm achieves a Nash equilibrium, which ensures the fairness of the corresponding rewards for parties. We conduct extensive experiments to evaluate the performance of our proposed framework against four baselines on five real-world datasets. The results show that IDEA outperforms state-of-the-art methods in terms of effectiveness, efficiency, and accumulated reward.

Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models without disclosing their raw data. In the vertical federated learning (VFL) setting, the collaborating parties have data from the same set of users but with disjoint attributes. After constructing the VFL models, the parties deploy the models in production systems to infer prediction requests. In practice, the prediction output itself may not be convincing for party users to make the decisions, especially in high-stakes applications. Model interpretability is therefore essential to provide meaningful insights and better comprehension on the prediction output. In this paper, we propose Falcon, a novel privacy-preserving and interpretable VFL system. First, Falcon supports VFL training and prediction with strong and efficient privacy protection for a wide range of ML models, including linear regression, logistic regression, and multi-layer perceptron. The protection is achieved by a hybrid strategy of threshold partially homomorphic encryption (PHE) and additive secret sharing scheme (SSS), ensuring no intermediate information disclosure. Second, Falcon facilitates understanding of VFL model predictions by a flexible and privacy-preserving interpretability framework, which enables the implementation of state-of-the-art interpretable methods in a decentralized setting. Third, Falcon supports efficient data parallelism of VFL tasks and optimizes the parallelism factors to reduce the overall execution time. Falcon is fully implemented, and on which, we conduct extensive experiments using six real-world and multiple synthetic datasets. The results demonstrate that Falcon achieves comparable accuracy to non-private algorithms and outperforms three secure baselines in terms of efficiency.

The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a M ultiview row- IN teractive T ime-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a time-aware behavior graph for each user's time-series relational data with each row represented as an action node. We utilize the user's temporal information to construct three different graph convolutional matrices for hierarchically learning the user's intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel gated neighbor interaction mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.

As the location-based applications ourishing, we will witness soon the transferring of a prodigious amount of data from the local to a public cloud. The rising demand for outsourced data is moving toward a wider geographical area with arbitrary distribution (i.e., dense or sparse) and query scope (i.e., limited or vast). In terms of cloud risks, the outsourced individual data should be preserved when being queried, especially for location information. Geometric range queries are one of the most fundamental search functions. However, the existed works of secure geometric queries are far from practical usage on efciency and security simultaneously. In this paper, we propose a novel scheme, LuxGeo. Our scheme reaches a constant navigation and a linear sweep, which is tailored for secure and efcient location-lookup. Our experiments over three real-world spatial datasets have shown its practical efciency. For example, it only takes 10.01s with 728 tuples retrieved over 63, 369 ciphertext dataset for a single query. LuxGeo has better performance than the existed solutions for a GSE problem on efciency and security.

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye. In recent work, however, Carlini et al. show that it is possible to reconstruct private images from the encrypted dataset generated by InstaHide. Nevertheless, we demonstrate that Carlini et al.’s attack can be easily defeated by incorporating data augmentation into InstaHide. This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. The basic idea is to use a comparative network to identify encrypted images that are likely to correspond to the same private image, and then employ a fusion-denoising network for restoring the private image from the encrypted ones, taking into account the effects of data augmentation. Extensive experiments demonstrate the effectiveness of the proposed attack in comparison to Carlini et al.’s attack.

Machine learning (ML) is an important part of modern data science applications. Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it makes their works available to end-users. Systems of model serving require high performance, low cost, and ease of management. Cloud providers are already offering model serving choices, including managed services and self-rented servers. Recently, serverless computing, whose advantages include high elasticity and a fine-grained cost model, brings another option for model serving.

With the increasing popularity of location-based services (LBS), data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers. Geometric range queries are one of the fundamental search functions in LBS, which are to find points inside geometric areas (e.g., circles or polygons). To ensure data confidentiality, the service users tend to encrypt the data before outsourcing it. However, regarding encrypted data, only a few consider geometric range queries, where the rationale is the high-dimension calculations make these queries particularly harder. In this paper, we propose a novel scheme for geometric range queries, that can provide the privacy of data stored at a cloud server and queries. Our scheme supports querying encrypted spatial data with irregular-shaped areas, achieves fast searches and enables dynamic updates. Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4× compared to state-of-the-art schemes, without carrying any potentially visible leakage in the structure.

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target's data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.

In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform efficient training. Both centralized and decentralized training approaches suffer from low-speed links. In this paper, we propose a decentralized approach, namely NetMax, that enables worker nodes to communicate via high-speed links and, thus, significantly speed up the training process. NetMax possesses the following novel features. First, it consists of a novel consensus algorithm that allows worker nodes to train model copies on their local dataset asynchronously and exchange information via peer-to-peer communication to synchronize their local copies, instead of a central master node (i.e., parameter server). Second, each worker node selects one peer randomly with a fine-tuned probability to exchange information per iteration. In particular, peers with high-speed links are selected with high probability. Third, the probabilities of selecting peers are designed to minimize the total convergence time. Moreover, we mathematically prove the convergence of NetMax. We evaluate NetMax on heterogeneous cluster networks and show that it achieves speedups of 3.7×, 3.4×, and 1.9× in comparison with the state-of-the-art decentralized training approaches Prague, Allreduce-SGD, and AD-PSGD, respectively.

Group k-nearest neighbor (kGNN) search allows a group of n mobile users to jointly retrieve k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving kGNN search in the full user collusion environment, where any n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider kGNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., kGNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.

As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. While the problem of secure range query on outsourced encrypted data has been extensively studied, the current schemes are far from the practice in terms of efficiency and scalability. In this paper, we propose a novel solution based on Geohash and predicate symmetric searchable encryption for secure range queries named as MixGeo. We present a multi-level indexes structure tailored for efficient and large-scale spatial data lookup in the cloud server while preserving data privacy.

Mobile crowd sensing has the potential to acquire massive data from places and address large-scale societal problems. However, most currently existing crowd sensing systems suffer from insufficient participants. Therefore, incentive design for crowd sensing is essential and urgent. In this paper, different from the auction-based and server-dominant incentives, we design a personalized incentive, PIE, with partiality for neither the server nor the participants with budget constraint. The total payment for all the participants accords to their collective participation level, and the individual reward for each participant depends on individual contribution. We measure the individual contribution and participation level based on Voronoi diagram and Shannon entropy. Both offline and online incentives are proposed with budget constraint. Experimental study shows that our incentives are participation-aware and contribution-dependent, which encourages participants' active join, balanced distribution and flexible reward.

Recently, listwise ranking-oriented collaborative filtering (CF) algorithms have gained great success in recommender systems. However, the ranked preference list may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantee is differential privacy. In this paper, we propose DPListCF, a differentially private algorithm based on ListCF (a state-of-art listwise CF algorithm). The main idea of DPListCF is to make both of the similarity calculation phase and rank prediction phase of ListCF satisfy differential privacy, by using input perturbation method and output perturbation method in the two phases respectively. Extensive experiments using two real datasets evaluate the performance of DPListCF, and demonstrate that the proposed algorithm outperforms state-of-art approaches.

Density estimation can construct an estimate of the probability density function from the observed data. However, such a function may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantees in data analysis is differential privacy. In this paper, we propose DPGMM, a parametric density estimation algorithm using Gaussian mixtures model (GMM) under differential privacy. GMM is a well-known model that could approximate any distribution and can be solved via Expectation-Maximization (EM) algorithm. The main idea of DPGMM is to add two extra steps after getting the estimated parameters in the M step of each iteration. The first step is the noise adding step, which injects calibrated noise to the estimated parameters according to their L1-sensitivities and privacy budgets. The second step is the post-processing step, which post-processes those noisy parameters that might break their intrinsic characteristics. Extensive experiments using both real and synthetic datasets evaluate the performance of DPGMM, and demonstrate that the proposed method outperforms a state-of-art approach.

Mobile devices have become more and more powerful with lots of embedded sensors providing sensibility of various data, which makes crowd sensing a compelling paradigm followed by emerging applications. In this paper, we design a crowd based incentive mechanism to stimulate participants joining sensing campaigns with consideration of location privacy. In our incentive, MagiCrowd, we creatively cluster the participants as several atomic crowds to bargain with the crowd sensing campaigner. We propose K-least to cluster the participants and achieve K-anonymity location privacy. Negotiated Bargain Theory is introduced to determine the incentive payoffs and Penalty Allocation is designed to allocate the gross crowd rewards. MagiCrowd can achieve high participation rate, rational reward and location privacy. We discuss the proposed incentive in depth and conduct thorough experiments to evaluate the desirable properties.