Mining Frequent Graph Patterns with Differential Privacy

Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. {\em Differential privacy} has recently emerged as the {\em de facto} standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that previous techniques on differentially private discovery of frequent {\em itemsets} cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into an MCMC sampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision

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

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

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

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

Thermal Performance of Solar Air Heater Having Absorber Plate with V-Down Discrete Rib Roughness for Space-Heating Applications

The paper presents results of thermal performance analysis of a solar air heater with v-down discrete rib roughness on the air flow side of the absorber plate, which supplies heated air for space heating applications. The air heater operates in a closed loop mode with inlet air at a fixed temperature of 295 K from the conditional space. The ambient temperature varied from 278 K to 288 K corresponding to the winter season of Western Rajasthan, India. The results of the analysis are presented in the form of performance plots, which can be utilized by a designer for calculating desired air flow rate at different ambient temperature and solar insolation values

FORMULATION AND EVALUATION OF TACROLIMUS LOADED TRANSFERSOMAL SUBLINGUAL FILMS FOR EFFICIENT MANAGEMENT OF ORGAN REJECTION: IN VITRO AND IN VIVO STUDY

Objective: Patients travailing with end-stage organ failure can benefit from life-saving treatment protocol called organ transplantation that also rallies eminence of life. Tacrolimus plays important role in maintaining the healthy status of the organ transplanted, but its widespread clinical application is constrained due to low oral bioavailability which can be the limiting factor for the reduction in life span of transplanted healthy organ. Methods: To overcome the drawbacks of tacrolimus and to maximize its therapeutic efficiency, tacrolimus was formulated as transfersomes using thin film hydration method using soyalecithin and  Tween-80,optimized by Central composite designs and characterized for Particle size, deformability index (DI), entrapment efficiency(EE%) and Zeta potential. The selected transfersome formulation was incorporated into sublingual films using Hydroxy Propyl Methyl Cellulose (HPMC) as film-forming polymer and Polyethylene Glycol (PEG-400) as Plasticizer. The physical characteristics (average weight, pH, uniformity of weight and thickness) of the prepared films were studied, in addition they were evaluated for the in vitro drug release, ex vivo permeation, Differential Scanning Calorimetry (DSC), Attenuated Reflectance Spectroscopy (ATR), Scanning Electronic Microscopy (SEM), stability and in vivo pharmacokinetics in rats to prove the effect of flexibility provided by vesicle formation through sublingual route for enhanced systemic availability of tacrolimus. Results: Designed and optimized transfersomal vesicles showed the vesicle size of 139±2.1 nm with Deformability Index of 8.53±1.9%, Entrapment Efficiency of 86.66±1.2% and zeta potential of -23.6 mV respectively. Optimized Tacrolimus-loaded transfersomal vesicles (TAC-TFs) showed controlled release with more than 80±3.4% for extended period of time compared to pure drug Tacrolimus. The average weight of all prepared transfersomal sublingual film (TAC_TF_SL films), batches were found in the range of 55.8±1.45 to 94.2±1.42 mg with mean thickness in the range of 0.23+0.1 to 0.52±0.2 mm indicating uniform cast of respective batches. The surface pH was found to be in the range of 6.9 to 7.0 which was close to saliva pH. Optimized transfersomal sublingual films as well as nanovesicular dispersions found to be followed Zero order, diffusion coupled with polymer relaxation. Ex vivo studies revealed the improved permeation of 6.51±0.04µg drug through sublingual mucosa than pure drug of 1.2±0.01 µg depicting the significant role of soyalecithin and edge activator in the formulations. Transfersomal sublingual films exhibited controlled release with higher plasma concentration of 9.16±2.34 µg/ml at Tmax of 1.29±1.51hr in comparison with 7.99±1.23 µg/ml at Tmax of 0.75±1.78hr (oral marketed dosage form Pengraf capsules) embarking the higher rate of controlled absorption after sublingual delivery of optimized sublingual films with significant increase in AUC of 129.87±2.40 μg/ml*hr when compared to AUC of marketed dosage form of 69.19 ±1.46 μg/ml*hr. In addition the absolute bioavailability of the drug following sublingual administration was found to be 70.77±2.92% in comparison with that after oral administration 40.60±2.34%. Conclusion: Designed Tacrolimus loaded transfersomal sublingual films can be a promising carrier for delivering tacrolimus through sublingual route by enhancing drug bioavailability efficiently which can be a boon to organ transplanted patients

Analyzing Graphs with Node Differential Privacy

Abstract. We develop algorithms for the private analysis of network data that provide accurate analysis of realistic networks while satisfying stronger privacy guarantees than those of previous work. We present several techniques for designing node differentially private algorithms, that is, algorithms whose output distribution does not change significantly when a node and all its adjacent edges are added to a graph. We also develop methodology for analyzing the accuracy of such algorithms on realistic networks. The main idea behind our techniques is to “project ” (in one of several senses) the input graph onto the set of graphs with maximum degree below a certain threshold. We design projection operators, tailored to specific statistics that have low sensitivity and preserve information about the original statistic. These operators can be viewed as giving a fractional (low-degree) graph that is a solution to an optimization problem described as a maximum flow instance, linear program, or convex program. In addition, we derive a generic, efficient reduction that allows us to apply any differentially private algorithm for bounded-degree graphs to an arbitrary graph. This reduction is based on analyzing the smooth sensitivity of the “naive ” truncation that simply discards nodes of high degree.