Machine learning algorithms, when applied to sensitive data, can pose severe threats to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose whether specific private data samples are present in the training data to an attacker. However, most existing studies on MIA focus on aggregated privacy leakage for an entire population, while leaving privacy leakage across different demographic subgroups (e.g., females and males) in the population largely unexplored. This raises two important issues: (1) privacy unfairness (i.e., if some subgroups are more vulnerable to MIAs than the others); and (2) defense unfairness (i.e., if the defense mechanisms provide more protection to some particular subgroups than the others).

Robustness and fairness are two equally important issues for machine learning systems. Despite the active research on robustness and fairness of ML recently, these efforts focus on either fairness or robustness, but not both. To bridge this gap, in this paper, we design Fair and Robust Classification (FRoC) models that equip the classification models with both fairness and robustness. Meeting both fairness and robustness constraints is not trivial due to the tension between them. The trade-off between fairness, robustness, and model accuracy also introduces additional challenge. To address these challenges, we design two FRoC methods, namely FRoC-PRE that modifies the input data before model training, and FRoC-IN that modifies the model with an adversarial objective function to address both fairness and robustness during training. FRoC-IN is suitable to the settings where the users (e.g., ML service providers) only have the access to the model but not the original data, while FRoC-PRE works for the settings where the users (e.g., data owners) have the access to both data and a surrogate model that may have similar architecture as the target model. Our extensive experiments on real-world datasets demonstrate that both FRoC-IN and FRoC-PRE can achieve both fairness and robustness with insignificant accuracy loss of the target model.

End-to-end email encryption (E2EE) ensures that an email could only be decrypted and read by its intended recipients. E2EE’s strong security guarantee is particularly desirable for the enterprises in the event of breaches: even if attackers break into an email server, under E2EE no contents of emails are leaked. Meanwhile, E2EE brings significant challenges for an enterprise to detect and filter unwanted emails (spams and phishing emails). Most existing solutions rely heavily on email contents (i.e., email body and attachments), which would be difficult when email contents are encrypted. In this paper, we investigate how to detect unwanted emails in a content-agnostic manner, that is, without access to the contents of emails at all.

Query processing over uncertain data has gained much attention recently. Due to the high computational complexity of query evaluation on uncertain data, the data owner can outsource her data to a server that provides query evaluation as a service. However, a dishonest server may return cheap (and incorrect) query answers, hoping that the client who has weak computational power cannot catch the incorrect results. To address the integrity issue, in this paper, we design AuthPDB, a framework that supports efficient authentication of query evaluation for both all-answer and top-k queries on outsourced probabilistic databases. Our empirical results on real-world datasets demonstrate the effectiveness and efficiency of AuthPDB.

Crowdsourcing systems have gained considerable interest and adoption in recent years. One important research problem for crowdsourcing systems is truth discovery, which aims to aggregate noisy answers contributed by the workers to obtain the correct answer (truth) of each task. However, since the collected answers are highly prone to the workers' biases, aggregating these biased answers without proper treatment will unavoidably lead to discriminatory truth discovery results for particular race, gender and political groups. To address this challenge, in this paper, first, we define a new fairness notion named θ-disparity for truth discovery. Intuitively, θ-disparity bounds the difference in the probabilities that the truth of both protected and unprotected groups being predicted to be positive. Second, we design three fairness enhancing methods, namely Pre-TD, FairTD, and Post-TD, for truth discovery. Pre-TD is a pre-processing method that removes the bias in workers' answers before truth discovery. FairTD is an in-processing method that incorporates fairness into the truth discovery process. And Post-TD is a post-processing method that applies additional treatment on the discovered truth to make it satisfy θ-disparity. We perform an extensive set of experiments on both synthetic and real-world crowdsourcing datasets. Our results demonstrate that among the three fairness enhancing methods, FairTD produces the best accuracy with θ-disparity. In some settings, the accuracy of FairTD is even better than truth discovery without fairness, as it removes some low-quality answers as side effects.

Many real-world social networks are decentralized in nature, and the only way to analyze such a network is to collect local views of the social graph from individual participants. Since local views may contain sensitive information, it is often desirable to apply differential privacy in the data collection process, which provides strong and rigorous privacy guarantees. In many practical situations, the local view of a participant contains not only her own connections, but also those of her neighbors, which are private and sensitive for the neighbors, but not directly so for the participant herself. We call such information beyond direct connections an extended local view (ELV)</>, and study two fundamental problems related to ELVs: first, how do we correctly enforce differential privacy for all participants in the presence of ELVs? Second, how can the data collector utilize ELVs to obtain accurate estimates of global graph properties?

Crowdsourcing is a new problem-solving paradigm for tasks that are difficult for computers but easy for humans. Since the answers collected from the recruited participants (workers) may contain sensitive information, crowdsourcing raises serious privacy concerns. In this paper, we investigate the problem of protecting user privacy under local differential privacy (LDP), where individual workers randomize their answers independently and send the perturbed answers to the task requester. The utility goal is to ensure high accuracy of the inferred true answers (i.e., truth) from the perturbed data. One of the challenges of LDP perturbation is the sparsity of worker answers (i.e., each worker only answers a small number of tasks). Simple extension of existing approaches (e.g., Laplace perturbation and randomized response) may incur large errors in truth inference on sparse data. Thus we design a new matrix factorization (MF) algorithm under LDP that addresses the trade-off between privacy and utility (i.e., accuracy of truth inference). We prove that our MF algorithm can provide both LDP guarantee and small error of truth inference, regardless of the sparsity of worker answers. We perform extensive experiments on real-world and synthetic datasets and demonstrate that the MF algorithm performs better than the existing LDP algorithms on sparse crowdsourcing data.

Crowdsourcing has raised several security concerns. One of the concerns is how to assign sensitive tasks in the crowdsourcing market, especially when there are colluding participants in crowdsourcing. In this paper, we consider adversarial colluding participants who intend to extract sensitive data by exchanging information. We design a 3-step sensitive task assignment method: (1) the collusion estimation step that quantifies the workers' pairwise collusion probability by estimating answer truth based on their responses; (2) the worker selection step that executes a heuristic sampling-based approach to select the fewest workers whose collusion probability satisfies the given security requirement; and (3) the task partitioning step that splits the sensitive information among the selected workers. We perform an extensive set of experiments on both real-world and synthetic datasets. The results demonstrate the accuracy and efficiency of our method.

Incentivized by the enormous economic profits, the data marketplace platform has been proliferated recently. In this paper, we consider the data marketplace setting where a data shopper would like to buy data instances from the data marketplace for correlation analysis of certain attributes. We assume that the data in the marketplace is dirty and not free. The goal is to find the data instances from a large number of datasets in the marketplace whose join result not only is of high-quality and rich join informativeness, but also delivers the best correlation between the requested attributes. To achieve this goal, we design DANCE, a middleware that provides the desired data acquisition service. DANCE consists of two phases: (1) In the off-line phase, it constructs a two-layer join graph from samples. The join graph includes the information of the datasets in the marketplace at both schema and instance levels; (2) In the online phase, it searches for the data instances that satisfy the constraints of data quality, budget, and join informativeness, while maximizing the correlation of source and target attribute sets. We prove that the complexity of the search problem is NP-hard, and design a heuristic algorithm based on Markov chain Monte Carlo (MCMC). Experiment results on two benchmark and one real datasets demonstrate the efficiency and effectiveness of our heuristic data acquisition algorithm.

Crowdsourced ranking algorithms ask the crowd to compare the objects and infer the full ranking based on the crowdsourced pairwise comparison results. In this paper, we consider the setting in which the task requester is equipped with a limited budget that can afford only a small number of pairwise comparisons. To make the problem more complicated, the crowd may return noisy comparison answers. We propose an approach to obtain a good-quality full ranking from a small number of pairwise preferences in two steps, namely task assignment and result inference. In the task assignment step, we generate pairwise comparison tasks that produce a full ranking with high probability. In the result inference step, based on the transitive property of pairwise comparisons and truth discovery, we design an efficient heuristic algorithm to find the best full ranking from the potentially conflictive pairwise preferences. The experiment results demonstrate the effectiveness and efficiency of our approach.