In aggregation queries, predicate parameters often reveal user intent. Protecting these parameters is critical for user privacy, regardless of whether the database is public or private. While most existing works focus on private data settings, we address a public data setting where the server has access to the database. Current solutions for this setting either require additional setups (e.g., non-colluding servers, hardware enclaves) or are inefficient for practical workloads. Furthermore, they often do not support range predicates or boolean combinations commonly seen in real-world use cases. To address these limitations, we built HADES, a fully homomorphic encryption (FHE) based private aggregation system for public data that supports point, range predicates, and boolean combinations. Our one-round HADES protocol efficiently generates predicate indicators by leveraging the plaintext form of public data records. It introduces a novel elementwise-mapping operation and an optimized reduction algorithm, achieving latency efficiency within a limited noise budget. Our highly scalable, multi-threaded implementation improves performance over previous one-round FHE solutions by 204x to 6574x on end-to-end TPC-H queries, reducing aggregation time on 1M records from 15 hours to 38 seconds.

Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Their profound capabilities in processing and interpreting complex language data, however, bring to light pressing concerns regarding data privacy, especially the risk of unintentional training data leakage. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Addressing this gap, our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs. LLM-PBE is designed to analyze privacy across the entire lifecycle of LLMs, incorporating diverse attack and defense strategies, and handling various data types and metrics. Through detailed experimentation with multiple LLMs, LLM-PBE facilitates an in-depth exploration of data privacy concerns, shedding light on influential factors such as model size, data characteristics, and evolving temporal dimensions. This study not only enriches the understanding of privacy issues in LLMs but also serves as a vital resource for future research in the field. Aimed at enhancing the breadth of knowledge in this area, the findings, resources, and our full technical report are made available at https://llm-pbe.github.io/, providing an open platform for academic and practical advancements in LLM privacy assessment.

Log-based cyber threat hunting has emerged as an important solution to counter sophisticated cyber attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external knowledge about threat behaviors provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we build ThreatRaptor, a system that facilitates cyber threat hunting in computer systems using OSCTI. Built upon mature system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query from the extracted threat behaviors, and (4) an efficient query execution engine to search the big system audit logging data.

Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query for hunting, and (4) an efficient query execution engine to search the big audit logging data. Evaluations on a broad set of attack cases demonstrate the accuracy and efficiency of ThreatRaptor in practical threat hunting.

To remain aware of the fast-evolving cyber threat landscape, open-source Cyber Threat Intelligence (OSCTI) has received growing attention from the community. Commonly, knowledge about threats is presented in a vast number of OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI gathering and management platforms, however, have primarily focused on isolated, low-level Indicators of Compromise. On the other hand, higher-level concepts (e.g., adversary tactics, techniques, and procedures) and their relationships have been overlooked, which contain essential knowledge about threat behaviors that is critical to uncovering the complete threat scenario. To bridge the gap, we propose SecurityKG, a system for automated OSCTI gathering and management. SecurityKG collects OSCTI reports from various sources, uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors, and constructs a security knowledge graph. SecurityKG also provides a UI that supports various types of interactivity to facilitate knowledge graph exploration.

A trojan backdoor is a hidden pattern typically implanted in a deep neural network (DNN). It could be activated and thus forces that infected model to behave abnormally when an input sample with a particular trigger is fed to that model. As such, given a DNN and clean input samples, it is challenging to inspect and determine the existence of a trojan backdoor. Recently, researchers design and develop several pioneering solutions to address this problem. They demonstrate that the proposed techniques have great potential in trojan detection. However, we show that none of these existing techniques completely address the problem. On the one hand, they mostly work under an unrealistic assumption of assuming the availability of the contaminated training database. On the other hand, these techniques can neither accurately detect the existence of trojan backdoors, nor restore high-fidelity triggers, especially when infected models are trained with high-dimensional data, and the triggers pertaining to the trojan vary in size, shape, and position. In this work, we propose TABOR, a new trojan detection technique. Conceptually, it formalizes the detection of a trojan backdoor as solving an optimization objective function. Different from the existing technique which also models trojan detection as an optimization problem, TABOR first designs a new objective function that could guide optimization to identify a trojan backdoor more correctly and accurately. Second, TABOR borrows the idea of interpretable AI to further prune the restored triggers. Last, TABOR designs a new anomaly detection method, which could not only facilitate the identification of intentionally injected triggers but also filter out false alarms (i.e., triggers detected from an uninfected model). We train 112 DNNs on five datasets and infect these models with two existing trojan attacks. We evaluate TABOR by using these infected models, and demonstrate that TABOR has much better performance in trigger restoration, trojan detection, and elimination than Neural Cleanse, the state-of-the-art trojan detection technique.

Nowadays an emerging class of applications are based oncollaboration over a shared database among different entities. However, the existing solutions on shared database may require trust on others, have high hardware demand that is unaffordable for individual users, or have relatively low performance. In other words, there is a trilemma among security, compatibility and efficiency. In this paper, we present FalconDB, which enables different parties with limited hardware resources to efficiently and securely collaborate on a database. FalconDB adopts database servers with verification interfaces accessible to clients and stores the digests for query/update authentications on a blockchain. Using blockchain as a consensus platform and a distributed ledger, FalconDB is able to work without any trust on each other. Meanwhile, FalconDB requires only minimal storage cost on each client, and provides anywhere-available, real-time and concurrent access to the database. As a result, FalconDB over-comes the disadvantages of previous solutions, and enables individual users to participate in the collaboration with high efficiency, low storage cost and blockchain-level security guarantees.

Malicious accounts are one of the biggest threats to the security and privacy of online social networks (OSNs). In this work, we study a new type of OSN, called privacy-centric mobile social network (PC-MSN), such as KakaoTalk and LINE, which has attracted billions of users recently. The design of PC-MSN is inspired to protect their users' privacy from strangers: (1) a stranger is not easy to send a friend request to a user who does not want to make friends with strangers; and (2) strangers cannot view a user's post. Such a design mitigates the security issue of malicious accounts. At the same time, it also brings the battleground between attackers and defenders to an earlier stage, i.e., making friendship, than the one studied in previous works. Also, previous defense proposals mostly rely on certain assumptions on the attacker, which may not be robust in the new PC-MSNs. As a result, previous malicious accounts detection approaches are less effective on a PC-MSN.

Given a data set D containing millions of data points and a data consumer who is willing to pay for $ X to train a machine learning (ML) model over D , how should we distribute this $X to each data point to reflect its "value"? In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all N data points, it requires O (2 N ) model evaluations for exact computation and O ( N log N ) for ( ϵ , δ)-approximation. In this paper, we focus on one popular family of ML models relying on K -nearest neighbors ( K NN). The most surprising result is that for unweighted K NN classifiers and regressors, the Shapley value of all N data points can be computed, exactly , in O ( N log N ) time - an exponential improvement on computational complexity! Moreover, for ( ϵ , δ)-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity O ( N h ( ϵ , K ) log N ) when ϵ is not too small and K is not too large. We empirically evaluate our algorithms on up to 10 million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further. We then extend our algorithm to other scenarios such as (1) weighed K NN classifiers, (2) different data points are clustered by different data curators , and (3) there are data analysts providing computation who also requires proper valuation. Some of these extensions, although also being improved exponentially, are less practical for exact computation (e.g., O ( N K ) complexity for weigthed K NN). We thus propose an Monte Carlo approximation algorithm, which is O ( N (log N ) 2 /(log K ) 2 ) times more efficient than the baseline approximation algorithm.

In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using privacy-preserving smart contracts which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned. For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.

Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of features and databases used in real-world SQL-based analytics systems.

Feature generation is one of the challenging aspects of machine learning. We present ExploreKit, a framework for automated feature generation. ExploreKit generates a large set of candidate features by combining information in the original features, with the aim of maximizing predictive performance according to user-selected criteria. To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features. This approach enables efficient identification of the new features and produces superior results compared to existing feature selection solutions. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 25 datasets and 3 different classification algorithms. We show that ExploreKit can achieve classification-error reduction of 20% overall. Our codeis available at https://github.com/giladkatz/ExploreKit.

The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we callSocial-Attribute Network(SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Google+ dataset, which we make publicly available (http://www.cs.berkeley.edu/~stevgong/gplus.html).

Android and Facebook provide third-party applications with access to users' private data and the ability to perform potentially sensitive operations (e.g., post to a user's wall or place phone calls). As a security measure, these platforms restrict applications' privileges with permission systems: users must approve the permissions requested by applications before the applications can make privacy-or security-relevant API calls. However, recent studies have shown that users often do not understand permission requests and are unsure of which permissions are typical for applications. As a first step towards simplifying permission systems, we cluster a corpus of 188,389 Android applications and 27,029 Facebook applications to find patterns in permission requests. Using a method for Boolean matrix factorization to find overlapping clusters of permissions, we find that Facebook permission requests follow a clear structure that can be fitted well with only five patterns, whereas Android applications demonstrate more complex permission requests. We also find that low-reputation applications often deviate from the permission request patterns that we identified for high-reputation applications, which suggests that permission request patterns can be indicative of user satisfaction or application quality.

It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data risks leaking information through its output. Differential privacy provides a theoretical framework for processing data while protecting the privacy of individual records in a dataset. Unfortunately, it has seen limited adoption because of the loss in output accuracy, the difficulty in making programs differentially private, lack of mechanisms to describe the privacy budget in a programmer's utilitarian terms, and the challenging requirement that data owners and data analysts manually distribute the limited privacy budget between queries.