Differentially Private Exponential Random Graphs

We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under $\epsilon$-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.Comment: minor edit

Assessing Centrality Without Knowing Connections

We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric betweenness centrality (EBC). Motivated by modern communication networks spanning multiple providers, we show for the first time how multiple mutually-distrusting parties can successfully compute node EBC while revealing only differentially-private information about their internal network connections. A theoretical utility analysis upper bounds a primary source of private EBC error---private release of ego networks---with high probability. Empirical results demonstrate practical applicability with a low 1.07 relative error achievable at strong privacy budget $\epsilon=0.1$ on a Facebook graph, and insignificant performance degradation as the number of network provider parties grows.Comment: Full report of paper appearing in PAKDD202

Learning to Sew: A Student Pharmacist’s Service-Learning Experience

Karolina Grzesiak is a fourth-year professional student in the College of Pharmacy at Purdue University and will earn her Doctor of Pharmacy degree in May 2017. She was raised in Poland but has called La Porte, Indiana home for the past eight years. Craig Vargo is a 2012 pharmacy graduate working as a clinical specialist pharmacist at the James Cancer Hospital at The Ohio State University Wexner Medical Center in Columbus, Ohio

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

Perceptions and Reactions with Regard to Pneumonic Plague

We assessed perceptions and likely reactions of 1,005 UK adults to a hypothetical terrorist attack involving pneumonic plague. Likely compliance with official recommendations ranged from good (98% would take antimicrobial drugs) to poor (76% would visit a treatment center). Perceptions about plague were associated with these intentions

Reversible Data Perturbation Techniques for Multi-level Privacy-preserving Data Publication

The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of privilege and access the data with a fixed privacy level. Thus, such schemes do not directly support data release in cases when data users have different levels of access on the published data. While a straight-forward approach of releasing a separate snapshot of the data for each possible data access level can allow multi-level access, it can result in a higher storage cost requiring separate storage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on privacy levels. The proposed schemes enable multi-level data privacy, allowing selective de-perturbation of the published data when suitable access credentials are provided. We evaluate the techniques through extensive experiments on a large real-world association graph dataset and our experiments show that the proposed techniques are efficient, scalable and effectively support multi-level data privacy on the published data

Horizontal Transmission of Candida albicans and Evidence of a Vaccine Response in Mice Colonized with the Fungus

Disseminated candidiasis is the third leading nosocomial blood stream infection in the United States and is often fatal. We previously showed that disseminated candidiasis was preventable in normal mice by immunization with either a glycopeptide or a peptide synthetic vaccine, both of which were Candida albicans cell wall derived. A weakness of these studies is that, unlike humans, mice do not have a C. albicans GI flora and they lack Candida serum antibodies. We examined the influence of C. albicans GI tract colonization and serum antibodies on mouse vaccination responses to the peptide, Fba, derived from fructose bisphosphate aldolase which has cytosolic and cell wall distributions in the fungus. We evaluated the effect of live C. albicans in drinking water and antimicrobial agents on establishment of Candida colonization of the mouse GI tract. Body mass, C. albicans in feces, and fungal-specific serum antibodies were monitored longitudinally. Unexpectedly, C. albicans colonization occurred in mice that received only antibiotics in their drinking water, provided that the mice were housed in the same room as intentionally colonized mice. The fungal strain in unintentionally colonized mice appeared identical to the strain used for intentional GI-tract colonization. This is the first report of horizontal transmission and spontaneous C. albicans colonization in mice. Importantly, many Candida-colonized mice developed serum fungal-specific antibodies. Despite the GI-tract colonization and presence of serum antibodies, the animals made antibodies in response to the Fba immunogen. This mouse model has potential for elucidating C. albicans horizontal transmission and for exploring factors that induce host defense against disseminated candidiasis. Furthermore, a combined protracted GI-tract colonization with Candida and the possibility of serum antibody responses to the presence of the fungus makes this an attractive mouse model for testing the efficacy of vaccines designed to prevent human disseminated candidiasis