Soft privacy technologies fall under the category of PETs, Privacy-enhancing technologies, as methods of protecting data. Soft privacy is a counterpart to another subcategory of PETs, called hard privacy. Soft privacy technology has the goal of keeping information safe, allowing services to process data while having full control of how data is being used. To accomplish this, soft privacy emphasizes the use of third-party programs to protect privacy, emphasizing auditing, certification, consent, access control, encryption, and differential privacy. Since evolving technologies like the internet, machine learning, and big data are being applied to many long-standing fields, we now need to process billions of datapoints every day in areas such as health care, autonomous cars, smart cards, social media, and more. Many of these fields rely on soft privacy technologies when they handle data.
Applications
Health care Some medical devices like Ambient Assisted Living monitor and report sensitive information remotely into a cloud. Cloud computing offers a solution that meets the healthcare need for processing and storage at an affordable price. Together, this system is used to monitor a patient's biometric conditions remotely, connecting with smart technology when necessary. In addition to monitoring, the devices can also send a mobile notification when certain conditions pass a set point such as a major change in blood pressure. Due to the nature of these devices, which report data constantly and use smart technology, this type of medical technology is subject to a lot of privacy concerns. Soft privacy is thus relevant for the third-party cloud service, as many privacy concerns center there, including risk in unauthorized access, data leakage, sensitive information disclosure, and privacy disclosure. One solution proposed for privacy issues around cloud computing in health care is through the use of Access control, by giving partial access to data based on a user's role: such as a doctor, family, etc. Another solution, applicable for wireless technology that moves data to a cloud, is through the usage of Differential privacy. The differential privacy system typically encrypts the data, sends it to a trusted service, then opens up access to it for hospital institutions. A strategy that is often used to prevent data leakage and attacks works by adding ‘noise’ into the data which changes its values slightly. The real underlying information can be accessed through security questions. A study by Sensors concluded that differential privacy techniques involving additional noise helped achieve mathematically-precise guaranteed privacy. Adding noise to data by default can prevent devastating privacy breaches. In the mid-90's the Commonwealth of Massachusetts Group Insurance Commission released anonymous health records while hiding some sensitive information such as addresses and phone numbers. Despite this attempt to hiding personal information while providing a useful database, privacy was still breached–by correlating the health databases with public voting databases, individuals' hidden data could be rediscovered. Without the differential privacy techniques of encrypting data and adding noise, it is possible to link data that may not seem related, together.
Autonomous cars Autonomous cars raise concerns about location tracking, because they are sensor-based vehicles. To achieve full autonomy, these cars would need access to a massive database with information on the surroundings, paths to take, interaction with others, weather, and many more circumstances that need to be accounted for. This leads to privacy and security questions: how the data will all be stored, who is it shared with, and what type of data is being stored. A lot of this data is potentially sensitive and could be used maliciously if it was leaked or hacked. Additionally, there are concerns over this data being sold to companies, as the data can help predict products the consumer would like or need. This could be undesirable as data here may expose health conditions and alert some companies to advertise to said customers with location-based spam or products. In terms of the legal aspect of this technology, there are rules and regulations governing some parts of these cars, but for other areas, laws are left unwritten or oftentimes outdated. Many of the current laws are vague, leaving the rules to be open to interpretation. For example, there are federal laws dating back many years ago governing computer privacy, which were extended to cover phones when they arose, and now are being extended again to apply to the “computer” inside most driverless cars. Another legal concern surrounds government surveillance: can governments gain access to driverless car data, giving them the opportunity for mass surveillance or tracking with no warrants? Finally, companies may try to use this data to improve their technology and target their marketing data to fit the needs of their consumers. In response to this controversy, automakers pre-empted government action on driverless cars with the Automotive Information Sharing and Analysis Center (Auto-ISAC) in August 2015 to establish protocols for cybersecurity and decide how to handle vehicular communication systems in a safe manner through with other autonomous cars. Vehicle-to-grid, known as V2G, plays a big role in the energy consumption, cost, and effectiveness of smart grids. This system is what electric vehicles use to charge and discharge, as they communicate with the power grid to fill up the appropriate amount. Although this ability is extremely important for electric vehicles, it does open up privacy issues surrounding the visibility of the car's location. For example, the driver's home address, place of work, place of entertainment, and record of frequency may be reflected in the car's charging history. With this information, which could potentially be leaked, there are a wide variety of security breaches that can occur. For example, someone’s health could be deduced by the car's number of hospital visits, or based on a user's parking patterns, they may receive location-based spam, or a thief could benefit from knowing their target's home address and work schedule. Like with handling sensitive health data, a possible solution here is to use differential privacy to add noise to the mathematical data so leaked information may not be as accurate.
Cloud storage
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