PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information and Knowledge Management (CIKM 2018

Preserving privacy in data analytics

Data Analytics is becoming an essential business tool for many data intensive companies and organizations. However, the increased use of such methods comes with the threat of data disclosure. Privacy-preserving methods have been developed with varying degrees of efficiency with the main goal of protecting individuals\u27 privacy. This tutorial aims at presenting models and techniques of preserving privacy in machine learning and data mining

Privacy-preserving Distributed Analytics: Addressing the Privacy-Utility Tradeoff Using Homomorphic Encryption for Peer-to-Peer Analytics

Data is becoming increasingly valuable, but concerns over its security and privacy have limited its utility in analytics. Researchers and practitioners are constantly facing a privacy-utility tradeoff where addressing the former is often at the cost of the data utility and accuracy. In this paper, we draw upon mathematical properties of partially homomorphic encryption, a form of asymmetric key encryption scheme, to transform raw data from multiple sources into secure, yet structure-preserving encrypted data for use in statistical models, without loss of accuracy. We contribute to the literature by: i) proposing a method for secure and privacy-preserving analytics and illustrating its utility by implementing a secure and privacy-preserving version of Maximum Likelihood Estimator, “s-MLE”, and ii) developing a web-based framework for privacy-preserving peer-to-peer analytics with distributed datasets. Our study has widespread applications in sundry industries including healthcare, finance, e-commerce etc., and has multi-faceted implications for academics, businesses, and governments

PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics

In this paper we propose Privacy-preserving User-data Bookkeeping & Analytics (PUBA), a building block destined to enable the implementation of business models (e.g., targeted advertising) and regulations (e.g., fraud detection) requiring user-data analysis in a privacy-preserving way. In PUBA, users keep an unlinkable but authenticated cryptographic logbook containing their historic data on their device. This logbook can only be updated by the operator while its content is not revealed. Users can take part in a privacy-preserving analytics computation, where it is ensured that their logbook is up-to-date and authentic while the potentially secret analytics function is verified to be privacy-friendly. Taking constrained devices into account, users may also outsource analytic computations (to a potentially malicious proxy not colluding with the operator).We model our novel building block in the Universal Composability framework and provide a practical protocol instantiation. To demonstrate the flexibility of PUBA, we sketch instantiations of privacy-preserving fraud detection and targeted advertising, although it could be used in many more scenarios, e.g. data analytics for multi-modal transportation systems. We implemented our bookkeeping protocols and an exemplary outsourced analytics computation based on logistic regression using the MP-SPDZ MPC framework. Performance evaluations using a smartphone as user device and more powerful hardware for operator and proxy suggest that PUBA for smaller logbooks can indeed be practical

From usability to secure computing and back again

Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a function while preserving the privacy of any individual party’s inputs to that function. As MPC protocols transition from research prototypes to realworld applications, the usability of MPC-enabled applications is increasingly critical to their successful deployment and widespread adoption. Our Web-MPC platform, designed with a focus on usability, has been deployed for privacy-preserving data aggregation initiatives with the City of Boston and the Greater Boston Chamber of Commerce. After building and deploying an initial version of the platform, we conducted a heuristic evaluation to identify usability improvements and implemented corresponding application enhancements. However, it is difficult to gauge the effectiveness of these changes within the context of real-world deployments using traditional web analytics tools without compromising the security guarantees of the platform. This work consists of two contributions that address this challenge: (1) the Web-MPC platform has been extended with the capability to collect web analytics using existing MPC protocols, and (2) as a test of this feature and a way to inform future work, this capability has been leveraged to conduct a usability study comparing the two versions ofWeb-MPC. While many efforts have focused on ways to enhance the usability of privacy-preserving technologies, this study serves as a model for using a privacy-preserving data-driven approach to evaluate and enhance the usability of privacy-preserving websites and applications deployed in realworld scenarios. Data collected in this study yields insights into the relationship between usability and security; these can help inform future implementations of MPC solutions.Published versio

Privacy-Preserving Cloud-Assisted Data Analytics

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users\u27 data may contain private information that needs to be protected. Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics. The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems. To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users. To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user\u27s data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee. The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee. In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Privacy-Preserving Data Mining and Analytics in Big Data

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets

Enabling analytics on sensitive medical data with secure multi-party computation

While there is a clear need to apply data analytics in the healthcare sector, this is often difficult because it requires combining sensitive data from multiple data sources. In this paper, we show how the cryptographic technique of secure multiparty computation can enable such data analytics by performing analytics without the need to share the underlying data. We discuss the issue of compliance to European privacy legislation; report on three pilots bringing these techniques closer to practice; and discuss the main challenges ahead to make fully privacy-preserving data analytics in the medical sector commonplace