From Physical to Cyber: Escalating Protection for Personalized Auto Insurance

Nowadays, auto insurance companies set personalized insurance rate based on data gathered directly from their customers' cars. In this paper, we show such a personalized insurance mechanism -- wildly adopted by many auto insurance companies -- is vulnerable to exploit. In particular, we demonstrate that an adversary can leverage off-the-shelf hardware to manipulate the data to the device that collects drivers' habits for insurance rate customization and obtain a fraudulent insurance discount. In response to this type of attack, we also propose a defense mechanism that escalates the protection for insurers' data collection. The main idea of this mechanism is to augment the insurer's data collection device with the ability to gather unforgeable data acquired from the physical world, and then leverage these data to identify manipulated data points. Our defense mechanism leveraged a statistical model built on unmanipulated data and is robust to manipulation methods that are not foreseen previously. We have implemented this defense mechanism as a proof-of-concept prototype and tested its effectiveness in the real world. Our evaluation shows that our defense mechanism exhibits a false positive rate of 0.032 and a false negative rate of 0.013.Comment: Appeared in Sensys 201

Psychosocial family factors and glycemic control among children aged 1-15 years with type 1 diabetes: a population-based survey

Background: Being the parents of children with diabetes is demanding. Jay Belsky’s determinants of parenting model emphasizes both the personal psychological resources, the characteristics of the child and contextual sources such as parents’ work, marital relations and social network support as important determinants for parenting. To better understand the factors influencing parental functioning among parents of children with type 1 diabetes, we aimed to investigate associations between the children’s glycated hemoglobin (HbA1c) and 1) variables related to the parents’ psychological and contextual resources, and 2) frequency of blood glucose measurement as a marker for diabetes-related parenting behavior. Methods: Mothers (n = 103) and fathers (n = 97) of 115 children younger than 16 years old participated in a population-based survey. The questionnaire comprised the Life Orientation Test, the Oslo 3-item Social Support Scale, a single question regarding perceived social limitation because of the child’s diabetes, the Relationship Satisfaction Scale and demographic and clinical variables. We investigated associations by using regression analysis. Related to the second aim hypoglycemic events, child age, diabetes duration, insulin regimen and comorbid diseases were included as covariates. Results: The mean HbA1c was 8.1%, and 29% had HbA1c ≤ 7.5%. In multiple regression analysis, lower HbA1c was associated with higher education and stronger perceptions of social limitation among the mothers. A higher frequency of blood glucose measurement was significantly associated with lower HbA1c in bivariate analysis. Higher child age was significantly associated with higher HbA1c both in bivariate and multivariate analysis. A scatterplot indicated this association to be linear. Conclusions: Most families do not reach recommended treatment goals for their child with type 1 diabetes. Concerning contextual sources of stress and support, the families who successfully reached the treatment goals had mothers with higher education and experienced a higher degree of social limitations because of the child’s diabetes. The continuous increasing HbA1c by age, also during the years before puberty, may indicate a need for further exploring the associations between child characteristics, context-related variables and parenting behavior such as factors facilitating the transfer of parents’ responsibility and motivation for continued frequent treatment tasks to their growing children