Utility-Based Privacy Preserving Data Publishing

Advances in data collection techniques and need for automation triggered in proliferation of a huge amount of data. This exponential increase in the collection of personal information has for some time represented a serious threat to privacy. With the advancement of technologies for data storage, data mining, machine learning, social networking and cloud computing, the problem is further fueled. Privacy is a fundamental right of every human being and needs to be preserved. As a counterbalance to the socio-technical transformations, most nations have both general policies on preserving privacy and specic legislation to control access to and use of data. Privacy preserving data publishing is the ability to control the dissemination and use of one's personal information. Mere publishing (or sharing) of original data in raw form results in identity disclosure with linkage attacks. To overcome linkage attacks, the techniques of statistical disclosure control are employed. One such approach is k-anonymity that reduce data across a set of key variables to a set of classes. In a k-anonymized dataset each record is indistinguishable from at least k-1 others, meaning that an attacker cannot link the data records to population units with certainty thus reducing the probability of disclosure. Algorithms that have been proposed to enforce k-anonymity are Samarati's algorithm and Sweeney's Datafly algorithm. Both of these algorithms adhere to full domain generalization with global recording. These methods have a tradeo between utility, computing time and information loss. A good privacy preserving technique should ensure a balance of utility and privacy, giving good performance and level of uncertainty. In this thesis, we propose an improved greedy heuristic that maintains a balance between utility, privacy, computing time and information loss. Given a dataset and k, constructing the dataset to k-anonymous dataset can be done by the above-mentioned schemes. One of the challenges is to nd the best value of k, when the dataset is provided. In this thesis, a scheme has been proposed to achieve the best value of k for a given dataset. The k-anonymity scheme suers from homogeneity attack. As a result, the l-diverse scheme was developed. It states that the diversity of domain values of the dataset in an equivalence class should be l. The l-diversity scheme suers from background knowledge attack. To address this problem, t-closeness scheme was proposed. The t-closeness principle states that the distribution of records in an equivalence class and the distribution of records in the table should not exceed more than t. The drawback with this scheme is that, the distance metric deployed in constructing a table, satisfying t-closeness, does not follow the distance characteristics. In this thesis, we have deployed an alternative distance metric namely, Hellinger metric, for constructing a t-closeness table. The t-closeness scheme with this alternative distance metric performed better with respect to the discernability metric and computing time. The k-anonymity, l-diversity and t-closeness schemes can be used to anonymize the dataset before publishing (releasing or sharing). This is generally in a static environment. There are also data that need to be published in a dynamic environment. One such example is a social network. Anonymizing social networks poses great challenges. Solutions suggested till date do not consider utility of the data while anonymizing. In this thesis, we propose a novel scheme to anonymize the users depending on their importance and take utility into consideration. Importance of a node was decided by the centrality and prestige measures. Hence, the utility and privacy of the users are balanced

Compulsive Buying Behavior and Online Shopping Addiction of Women

The rise of e-commerce and online shopping platforms has revolutionized the way we shop and make purchases. While the convenience and accessibility of online shopping have been a boon for consumers, it has also led to the rise of compulsive buying behavior and online shopping addiction among women. The purpose of this paper is to explore the phenomenon of compulsive buying behavior and online shopping addiction among women and to understand the factors that contribute to this problem. This paper provides a comprehensive review of the literature on compulsive buying behavior and online shopping addiction using a purposeful sample of two hundred women who consider themselves to be addicted to online shopping. The findings from this paper can help shed light on the issue and inform future research and interventions aimed at addressing compulsive buying behavior and online shopping addiction among women

Phocomelia: is it time to retrospect, regulate and rescue? a case report

Phocomelia is an extremely rare congenital anomaly which presents as, the proximal part of the limb (humerus or femur, radius or tibia, ulna or fibula) being absent or markedly hypoplastic, with normal or near normal hand or foot. True phocomelia presents as the total absence of the intermediate segments of the limb, with the hand or foot directly attached to the trunk. Presented here is a case of phocomelia in an aborted foetus, with no maternal history of thalidomide exposure in her pregnancy and for whom evaluation of other family members/ siblings failed to reveal any substantial abnormality. The differential diagnosis and the significance of reporting of serious adverse drug reactions are discussed

An approach to compute user similarity for GPS applications

The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K-nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K-neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature

Hidden location prediction using check-in patterns in location based social networks

Check-in facility in a Location Based Social Network (LBSN) enables people to share location information as well as real life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user's checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state of the art work in literature

Return and Volatility Spillovers of Asian Pacific Stock Markets’ Energy Indices

The aim of the study was to investigate the presence of volatility among the Energy Indices of Asia Pacific Stock Markets. To test the volatility among the daily returns of Energy Indices of Asia Pacific Stock Markets, the study selected five sample Asian Pacific stock markets’ Energy Indices on the basis of availability of data. The findings of descriptive statistics and the ADF Test revealed, that the daily returns of the sample energy indices of Asian Pacific stock markets were not normally distributed and achieved stationarity at level difference, over the research period. Hence the data may be used for additional analysis. The data were then analysed, by using the GARCH (1,1) model to assess the considerable volatility of daily returns of sample energy indices and the study, which revealed that during the study period, all of the sample energy indices were volatile

An approach to compute user similarity for GPS applications

This paper was accepted for publication in the journal Knowledge-Based Systems and the definitive published version is available at http://dx.doi.org/10.1016/j.knosys.2016.09.017.The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K-nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K-neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature

Effect of COVID-19 Pandemic on NSE Nifty Energy Index

The research intends to assess the efficiency of NSE Energy Index-listed firms throughout the COVID-19 before and post pandemic phases, which run from 2019 to 2021. The primary goal of this article was to examine the price movement of corporations in the petroleum, gas, and electricity sectors by employing statistical methods such as descriptive statistics, ADF, and the GARCH (1,1) model, during the period of study. When comparing the post-COVID-19 pandemic era to the pre-COVID-19 pandemic period, certain firms experienced excessive volatility. The energy market's investor sentiment was significantly higher on the tail events, suggesting that anxious investors raced to put options and paid an exorbitant premium to shield them against unprecedented danger in the energy market