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

Performing the Discourse of Sexuality Online

Though sometimes pitted against one another and at times contradictory, the ideas of Michel Foucault and Judith Butler on the nature and expression of our sexuality and our gender identities help us to gain a deeper and more rounded picture of the impact and import of the burgeoning phenomenon of internet dating websites. This paper looks at the use and prevalence of video-sharing technologies on sexual social networking websites, in the context of notions of sexual identity and an information systems approach to the phenomenon of internet dating

Identifying Behavioral Differences Between People With and Without Previous Cancer Diagnosis

We undertake a study to determine and assess the effects of the statistically significant predictors of the behaviors and notions that are associated with a cancer diagnosis using the 2014 Health Information National Trends Survey (HINTS) data. We implemented a new and extensive logistic regression modeling using stepwise variable selection and jackknife parameter estimation that identified the best explanatory model. Our results show that age, average time spent watching TV or playing games, usage of sunscreen, fruit intake intent, and the opinion-based variables for behaviors affecting high blood pressure, as well as the participant preference of not knowing the chance of getting cancer are the optimal set of covariates impacting the chance of getting cancer. Moreover, using more sunscreen, and a higher age was associated with increases in the chances of getting cancer. Interestingly, many usually important background covariates such as race, income, gender, geographical location, and others were not significant predictors of the outcome variable of interest. The conclusions of our analysis reveal new insights in the complexity of the behaviors and “attitudes” associated with a higher chance of a cancer diagnosis and will undoubtedly have important implications on the design and success of future healthcare messages and campaigns

Human choice and computers : an ever more intimate relationship

Since 1974, the Human Choice and Computers (HCC) conference series has firmly remained at the cutting edge of innovative thinking about the interface between the social and technology. This introductory chapter to the proceedings of the 12th Human Choice and Computers conference points out that what has set HCC conferences apart is the critical perspective that is its hallmark. HCC12 continues this tradition