Data Privacy And Security In Autonomous Cars
As the development and testing of self-driving car technology has progressed, the prospect of privately-owned autonomous vehicles operating on public roads is nearing. Industry experts predict that autonomous vehicles will be commercially available within the next five to ten years. As automation becomes more prevalent in the transportation industry, driverless vehicles are appearing more frequently in the news. Asymmetric algorithms have shown their impact on large amount of data that have been secured by generating a public key. Cyber security is the major concern for any autonomous vehicle. The main contribution of this research is to secure data using an Asymmetric algorithm technique which will be saved in cloud storage.
Introduction
Driverless vehicles are currently gaining momentum not only in industry, but also in the public forum. The media coverage on driverless transportation, especially cars, has increased over the past decades. A growing number of news outlets are not only focusing on companies developing automated driverless technologies, but also consumer experiences with driverless cars. For example, recent news articles have reported self-driving cars with topics ranging from the technology advancements for self-driving vehicles, to the benefits of self-driving vehicles for the elderly and those with disabilities, and of course to the catastrophic – a self-driving car killing a pedestrian. Driverless cars are frequently reported on as new developments happen, whether they are technological developments, social developments, or policy developments. However, not all development is positive. With the increasing capability of driverless technologies (i. e. automated braking systems, lane change assist) there is also a certain level of accountability – companies’ products are expected to work properly and safely. In the case of automated driving technologies, it is immediately clear to the designers, engineers, and the consumers when these products are unreliable or unsafe. This is due largely in part to the role of the media in shaping consumer perceptions of driverless cars. News headlines may be biased a certain way, which can benefit or hinder public perception of these cars; therefore they will likely play a large role in swaying consumer perceptions and behaviours.
Self-driving cars, or autonomous vehicles, may be the greatest disruptive innovation to travel that we have experienced in a century. A fully-automated, self-driving car is able to perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Self-driving cars have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. However, obstacles remain for the full implementation of the technology including the need to reduce public fear, increase reliability, and create adequate regulations.
Of particular concern with regard to self-driving cars are Data privacy and Cyber Security risks. To date, five states and the District of Columbia have enacted laws that address autonomous vehicles or autonomous technology, but none of these state regulations address key areas of data privacy and security, such as the collection, use, choice, and security of consumer data gathered from these autonomous vehicles or autonomous technology. As vehicles become more computerized and begin to generate huge amounts of data, the potential for unwanted third party access of that data and the risk of cyber threat increases. Hackers could potentially access the personal data of a driver, such as the vehicle’s location, the identity of others in the car, and whether the driver is home at any particular time. Additionally, cyber -attacks could have potentially fatal consequences, not just for the driver and passengers inside the vehicle, but for anyone or anything physically surrounding the self-driving car. The model is based on cloud computing and for security with RSA algorithm and using ORAM for more privacy. Oblivious Random Access Memory (ORAM) is a security mechanism to hide data access patterns. ORAM with RSA algorithm has given more successful output for multipurpose cloud architectures of online shopping and banking systems.
Cloud computing has become one of the most exciting platforms to use when it comes to the everyday tasks of an end user. The reasons for such are various due to its high scalability, enablement of ubiquitous capabilities, and the feature of having on-demand access to a shared pool of computing resources. It has become a vital part to usability as well as rapid access over interconnected networks in fields such as web development, big data analytics, and IoT.
Though cloud computing has made a substantial contribution to the technical field, there are risks and issues associated with it of which include, but not limited to, security, privacy, and secure management of sensitive information in transmission.
Autonomous vehicles are the future of transportation, offering a variety of social benefits such as improving mobility for the elderly, disabled and children, reducing accidents caused by driver errors, decreasing congestion linked to selfish driving behaviour, increasing fuel efficiency by reducing unnecessary braking, and enhancing human productivity by freeing us from driving. To acquire real-time information about their environment, autonomous vehicles use an array of sensing technologies, e. g. , sonar devices, cameras, lasers, and radars. However, because sensor data may be inaccurate or incomplete due to limited range, field of view, and obstructions. Vehicles can retrieve information that assist autonomous driving (e. g. , map publishers) through vehicle-to-infrastructure (V2I).
Data privacy and security issues in autonomous cars at various levels:
Location Tracking
Location data is necessarily collected and used in autonomous vehicles for navigation purposes – e. g. , destination information, route information, speed, and time travelled. Location features are also used in existing traditional vehicles to remember locations; provide additional information relevant to the trip, such as real-time traffic data and points-of-interest along the planned route; and to set routing preferences, such as avoiding highways or toll roads.
A data set that correlates location and travel data (e. g. , current location, destination, speed, route, date and time) with additional information about the owner and passenger, could provide various benefits. For example, this type of data set may help in traffic planning, reducing congestion, and improving safety. However, this type of combined data set may also reveal sensitive information about individuals, particularly if this information is maintained over time. These privacy concerns exist both at the individual and societal level. The Supreme Court has already recognized in U. S. v. Jones that location information “generates a precise, comprehensive record of a person’s public movements that reflects a wealth of detail about her familial, political, professional, religious, and sexual associations. ” The New York Court of Appeals noted that an individual’s historical location and destination information would reveal “indisputably private” trips, such as to a psychiatrist, plastic surgeon, abortion clinic, AIDS treatment centre, strip club, criminal defence attorney, by-the-hour motel, union meeting, place of worship, and gay bar. Aside from the revelation of private information, information about one’s present location or travel patterns may create a risk of physical harm or stalking if that information fell into the wrong hands.
Sensor Data
Autonomous vehicles (and existing human-driven vehicles) contain sensors that collect data about the vehicle’s operation and its surroundings. For example, sensors in Google’s self-driving car include cameras, radar, thermal imaging devices, and light detection and ranging (LIDAR) devices that collect data about the environment outside the vehicle. Among other uses, this data helps autonomous vehicles determine the objects it encounters, make predictions about the environment, and take action based on these predictions.
Companies that continuously collect data about a vehicle’s surroundings may wish to bear in mind lessons from past enforcement actions. For instance, in 2012, the FCC sanctioned Google for gathering Wi-Fi network information and the content of data transmitted over Wi-Fi networks (including sensitive information, such as email content, websites visited, and passwords) as its Google Street View vehicles gathered images of public roads. Autonomous vehicles are capable of collecting driving habits, destinations and other revealing information about other drivers without their knowledge or consent. Additional concerns could arise based on the use of imagery captured by the vehicle, including ownership disputes and potential invasion of privacy claims, depending upon the circumstances in which the images are captured. Thus, companies engaging in broad data collection may wish to implement safeguards to protect individual privacy.
Third party collection and use of Data
In 2014, Jim Farley, the Global Vice-President of Marketing and Sales at Ford, told attendees of the Consumer Electronics Show: “We know everyone who breaks the law, we know when you’re doing it. We have GPS in your car, so we know what you’re doing. By the way, we don’t supply that data to anyone. ” Although he later retracted the statement, Farley’s comments highlight the privacy implications of data collection and use. Indeed, personal information about an autonomous vehicle user’s locations and on-road behaviour may be valuable to various government and private sector entities including law enforcement, news media, private investigators, and insurance companies. From a law enforcement perspective, permitting access to sensor data presents many of the same privacy concerns as devices like traffic cameras, including the ability to track an individual’s location or, as suggested by Farley’s comments, the ability to identify when individuals may have violated traffic or other laws. Additionally, sensor data may provide important information to insurance companies. For example, insurers could monitor driving habits and adjust premiums, similar to a voluntary program currently offered by one auto insurer. Insurers may also be interested in leveraging both internal sensors (e. g. , those that track speed and bearing of the vehicle) and external sensors (e. g. , cameras or LIDAR) in connection with accident investigations. The issue of ownership of this data may impact whether the insurer has a right to acquire or use this data. Autonomous vehicles also present challenging questions related to another insurance consideration — liability. Various commentators have identified many of these legal issues, including whether the driver, manufacturer or software would be held liable in the event of a collision.
Conclusion
For autonomous cars privacy and data security are major concerns. There are many states in United States of America who has autonomous cars but they have same major issue, as autonomous cars use different sensors, cameras, information of users to collect data of surrounding, but this data is still not secured, so there are chances cyber threat or attack using this data gathered by autonomous cars. Data security and privacy after using data secured techniques with RSA algorithm to encrypt and decrypt the data and storing it in cloud it will be more secured system. As this method has shown good results for multi cloud system for online shopping and banking systems. Information security and protection will be expanded, Because of accessibility the proportion of execution will get increment and transport arrangement of self-governing vehicles. Less odds of mischances. This framework will be more secured for any sort of digital assault, digital risk is the real worry of this framework. Organizations of this independent vehicles will be less singed because of information protection and security. This information assembled by the self-sufficient vehicle will be useful in crisis circumstances.
