Depending on which late-model vehicle you own, your car might be watching you — literally and figuratively — as you drive down the road. It’s watching you with cameras that monitor the cabin and track where you’re looking, and with sensors that track your speed, lane positions and rates of acceleration.
In addition to providing these functions, this data collection is a potential privacy nightmare. The information can reveal your identity, your habits when you’re in your car, how safely you drive, where you’ve been and where you regularly go.
There is a trade-off (权衡) between the quality of the driving experience and the privacy of drivers, depending on the level of services and features. Some drivers may prefer to share their biometric data to facilitate accessing a car’s functions and automating a major part of their driving experience. Others may prefer to manually control the car’s systems, sharing less personally identifiable information or none at all.
At first glance, it seems the trade-off between privacy and driver comfort cannot be avoided. Car manufacturers tend to take measures to protect drivers’ data against data thieves, but they collect a lot of data themselves. And as the Mozill a Foundation report showed, most car companies reserve the right to sell your data.
Researchers are now working on developing data analytics tools that better protect privacy and make progress on eliminating the trade-off. For instance, over the past seven years, the concept of federated machine learning has attracted attention because it allows algorithms (算法) to learn from the data on your local device without copying the data to a central server. Google’s Gboard keyboard benefits from federated learning to better guess the next word you are likely to type without sharing your private data with a server. There are other techniques to preserve privacy as well, such as location obfuscation, which alters the user’s location data to prevent the location from being revealed.
While there is still a trade-off between user privacy and quality of service, privacy-preserving data analytics techniques could pave the way for using data without leaking drivers’ and passengers’ personally identifiable information. This way, drivers could benefit from a wide range of modern cars’ services and features without paying the high cost of losing privacy.
【小题1】What is the purpose of the first paragraph?| A．To explain the benefits of your car. |
| B．To bring in the topic of privacy problems. |
| C．To point out the bright future of car industry. |
| D．To stress the advanced technology applied in cars. |
| A．A good medicine tastes bitter. | B．Knowledge starts with practice. |
| C．A fall into a pit, a gain in your wit. | D．One man’s meat is another man’s poison. |
| A．It can protect user privacy. | B．It makes algorithms learn fast. |
| C．It is yet to be put into application. | D．It copies local data to a central server. |
| A．Hesitant. | B．Indifferent. | C．Positive. | D．Objective. |
