In a stalking scenario, the prior relationship between and the gender of stalker and victim were systematically manipulated in order to judge culpability and consequences for the persons involved. Written vignettes were presented to 168 participants who responded via seven Likert scales. Stalker-victim relationship had three levels: ex-partner, acquaintance and stranger. In accordance with the 'Just World' hypothesis (Lerner, 1980), the victim was judged as having greater responsibility for the stalking when their harasser was an ex-partner or a prior acquaintance rather than a stranger, and police intervention was felt to be most necessary when the stalker was a stranger. Sex of stalker and victim was manipulated, and the following comparisons proved significant: when the perpetrator was male, bodily injury to the victim was seen as more likely and police intervention as more necessary than when the perpetrator was female; and male victims were viewed as more responsible for the scenario and as possessing greater powers to alleviate it. The Just World hypothesis and gender stereotypes provide a plausible account for these findings. Future research should determine whether criminal convictions show similar biases towards convicting male and stranger stalkers more often than female and ex-partner stalkers.

Purpose. Sensational interests (e.g. an interest in the occult or the methods of violence) in mentally disordered offenders are claimed to signify greater risk of psychopathology, but evidence to support this view is slight. Methods. The relationships between self‐reported DSM‐IV personality disorder (PD), general personality traits and sensational interests were examined in 155 of 167 consecutively referred offenders to a forensic psychology service. The subscales of the PD and personality trait measures were reduced to the four basic PD/trait dimensions (asocial, antisocial, anxious and anankastic) using confirmatory factor analysis. Results. Those high on the ‘antisocial’ factor (which was primarily defined by low Agreeableness, low Conscientiousness, and substantial elements of Paranoid, Antisocial and Borderline PD) were more interested in ‘violent‐occult’ and militaristic topics. Conclusions. The aspects of the antisocial factor primarily associated with an interest in sensational and potentially violent topics cover a wide range of putative disorders. However, the factors reflecting asocial, anxious or anankastic disorders do not show a reliable association with measures of sensational interests. These results suggest that the personality dimensions reflecting an interest in ‘sensational’ topics in mentally disordered offenders are relatively specific.

Purpose. Laymen and legal professionals frequently make decisions on the culpability of drivers involved in collisions on the basis of incomplete and inconsistent information. Could attributions based on car and driver stereotypes influence decisions on culpability? Methods. In Experiment 1, ratings were collected on the perceived on‐road aggressiveness of drivers of different age and gender, and for models and colours of motorcars driven. In Experiment 2, participants read an accident scenario involving two cars and were asked to estimate relative speed, position on the road and blame. The ages of the drivers, colours, make and model of car driven were manipulated using the aggressiveness ratings collected in Experiment 1. In Experiment 3, participants read another scenario and were again invited to allocate blame; colour, model of car and driver's age were varied systematically to establish the relative contribution of the different elements of the stereotype. Results. Combinations of colour, car and driver rated high on aggression were judged as travelling faster, being further across the road and more likely to be the cause of an accident than those rated low on these dimensions. Conclusions. Pre‐existing car and driver stereotypes have a demonstrable influence on judgments of driver behaviour from conflicting accident statements. The possible implications for the handling of accident claims and legal cases are discussed.

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Privacy preservation in Data Mining has become more prominent and popular because of its property of maintaining privacy of sensitive data for analysis purposes. In this decade, enormous volume of data is created by many sectors especially healthcare, and it is vital to analyze and extract the right information out of it. For instance, the integration of patient's medical records and health test data helps to identify the relation between atypical test result and disease. Incorporating association rule mining on this data aids in creating new information which contributes in disease prevention. During association rule mining procedure, it is crucial to maintain the privacy and security of data, the business's vital information should not be leaked. In this paper, we provide an effective solution of privacy preservation along with association rule mining. Our paper is focused on healthcare datasets; however, it can be extended and implemented in various areas Index Terms-Data Mining, Privacy Preserving, Association Rule Mining, Cryptography I. INTRODUCTION (A)Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. It has attracted a great deal of attention in the information industry and in society. Data mining includes the utilization of refined data analysis tools to find previously unknown, valid patterns and relationships in huge data sets. These tools can incorporate statistical models, machine learning techniques, and mathematical algorithms, such as neural networks or decision trees. Thus, data mining incorporates analysis and prediction. In recent data mining projects, various major data mining techniques have been developed and used, including association, classification, clustering, prediction, sequential patterns, and regression. (B) Data Partition ModelPartitioning a data set is splitting the data into two, sometimes three smaller data sets. These are called Training, Validation and Test. This technique is best practice when creating a predictive model but is only possible when working with enough data. Test data sets are less common due the volume of data required. If a predictive model is created to fit a specific data set, it is possible to create a highly predictive model. To ensure that this model will predict new data well, it should be tested on a different sample of data to see how accurate it is. Data partitioning is used to split the original data set before the model is created so that there is 'new' data available to assess the model.In distributed environment, two major types of data partition model among all participants that is, Horizontal Partitioned and Vertical Partitioned data model are given.Here, we discuss some of the existing approaches towards privacy persevering distributed association rule mining of both kinds ofdatapartitioning. In the horizontally partitioned data, all the participants have the same schema, but each participant contains the records of different entities.