Increased Automotive Data Use Raises Privacy, Security Concerns

The amount of data being collected, processed, and stored in vehicles is exploding, and so is the value of that data. That raises questions that are still not fully answered about how that data will be used, by whom, and how it will be secured.

Automakers are competing based on the latest versions of advanced technologies such as ADAS, 5G, and V2X, but the ECUs, software-defined vehicles, and in-cabin monitoring also demand more and more data, and they are using that data for purposes that extend beyond just getting the vehicle from point A to point B safely. They now are vying to offer additional subscription-based services according to customers’ interests, as various entities, including insurance companies, indicate a willingness to pay for information on drivers’ habits.

Collecting this data can help OEMs gain insights and potentially generate additional revenue. However, gathering it raises privacy and security concerns about who will own this massive amount of data and how it should be managed and used. And as automotive data use increases, how will it impact future automotive design?

Fig. 1: Connected vehicles rely on software to communicate between vehicles and the cloud. Source: McKinsey & Co.

“Much of the data generated in the vehicle will have immense value to OEMs and their partners for analyzing driver behavior and vehicle performance and for developing new or enhanced features,” said Sven Kopacz, autonomous vehicle section manager at Keysight Technologies. “On the other hand, the privacy of data use can be viewed as a risk to some. But the real value – as already implemented and used by Tesla and others – is the constant feedback to improve those ADAS algorithms, enable a CI/CD DevOps software development model, and allow the rapid download of updates. Only time will tell if law enforcement and the courts will demand this data and how lawmakers will respond.”

Types of data generated
According to Precedence Research, the global automotive data market size will grow from $2.19 billion in 2022 to $14.29 billion by 2032, with many types of data collected, including:

• Autonomous driving: Data on all levels, from L1 to L5, including that collected from the multiple sensors installed on vehicles.
• Infrastructure: Remote monitoring, OTA updates, and data used for remote control by control centers, V2X, and traffic patterns.
• Infotainment: Information on how customers are using applications, such as voice control, gesture, maps, and parking.
• Connected information: Information on payment to third-party parking apps, accident information, data from dashboard cameras, handheld devices, mobile applications, and driver behavior monitoring.
• Vehicle health: Repair and maintenance records, insurance underwriting, fuel consumption, telematics.

This information may be useful for future automotive design, predictive maintenance, and safety improvements, and insurance companies are expected to be able to reduce underwriting costs with more comprehensive information on accidents. Based on the information collected, OEMs should be able to design more reliable and safer cars, and to stay in close touch with customer wants. For example, experiments can be conducted to gauge customer demand for subscription-based services such as automatic parking and more sophisticated voice input and commands.

“Diagnostic data for service and repair has been a core of automotive data analytics for decades,” noted Lorin Kennedy, senior staff product management manager for SLM in-field analytics at Synopsys. “With the advent of connected vehicles and advanced machine learning (ML) analytics, which enable a greater quantity of data to be routinely processed, this data has gained exponentially in value. As data drives feature enhancements such as mobile-like experiences and advanced driver assist capabilities, OEMs increasingly need to better understand the dependability and reliability of the semiconductor systems powering these new features. The collection of monitoring and sensor data from electronic components and the semiconductors themselves will be a growing diagnostic data requirement across all types of automotive technologies like ADAS, IVI, ECUs, etc. to ensure quality and reliability on these more advanced nodes.”

Anticipated updates to ISO 26262 regulations regarding the application of predictive maintenance to hardware, identifying degrading intermittent faults caused by silicon aging, and over-stress conditions in the field are areas to be addressed, as well. Those can include silicon lifecycle management (SLM) technologies, which can deliver more comprehensive knowledge about the health and remaining useful life of silicon as it ages.

“That knowledge, in turn, will enable service updates and future OTA releases that leverage additional semiconductor compute power,” Kennedy said. “Overall fleet performance will benefit, and the semiconductor and system design process will, too, as new insights help achieve greater efficiencies. OEM, Tier One, and semiconductor supplier collaboration on what the data brings to light – from silicon to software system performance – will enable vehicles to meet the functional safety design parameters that are becoming increasingly crucial in advanced electronics.”

Still, for data generated in vehicles, OEMs will need to prioritize which data can provide value for drivers immediately, and which data should be sent to the cloud via 5G connections.

“Tradeoffs between on-board processing to reduce data volume and data transmission network costs will likely dictate prioritization,” Keysight’s Kopacz said. “For example, camera, lidar, and radar sensor data for ADAS applications may have value for training ADAS algorithms, but the volume of raw data will be very costly to transmit and store. Likewise, driver attention data can have high value in UI design, and would be best gathered in a meta-data form. V2X data has a relatively lower data volume and should ultimately be a key data source for ADAS, providing in-car non-line-of-sight visibility of other vehicles, road infrastructure, and road conditions. Sharing this over V2N links can enable effective safety applications, but angle random walk (ARW) sensor data needs to be considered more carefully due to its complex nature. Infotainment streaming content into the vehicle also can be a valuable revenue stream for OEMs, and the content providers as well, as network operators working together.”

Impacts on automotive cybersecurity
As vehicles become more autonomous and connected, data use will increase, and so will the value of that data. This raises cybersecurity and data privacy concerns. Hackers want to steal personal data collected by the vehicles, and can use ransomware and other attacks to do so. The idea of taking control of vehicles — or worse, stealing them — also attracts hackers. Techniques used include hacking vehicle apps and wireless connections on the vehicles (diagnostics, key fob attacks and keyless jamming). Protecting data access, vehicles, and infrastructure from attacks is increasingly important and challenging.

Cybersecurity risks increase with software-defined vehicles. Memory especially will need to be safeguarded.

“The integration of advanced technology into EVs poses significant cybersecurity challenges that demand immediate attention and sophisticated solutions,” said Ilia Stolov, center head of secure memory solution at Winbond. “Central to the digital fortresses within modern electronic platforms are flash non-volatile memories, housing invaluable assets like code, private data, and company credentials. Unfortunately, their ubiquity has rendered them attractive targets for hackers seeking unauthorized access to sensitive information.”

Stolov noted that Winbond has been actively working to secure flash memory from hacks.

Additionally, there are important considerations in securing memory designs, such as:

• DICE root of trust: The Device Identifier Composition Engine (DICE) should be used to create the secure flash root of trust for hardware security. This secure identity forms the basis for building trust in the hardware. Other security measures can therefore rely on the authenticity and integrity of the boot code, protecting against firmware and software attacks. The initial boot process and subsequent software execution are based on trusted and verified measurements, helping prevent the injection of malicious code into the system.
• Code and data protection: Protecting code and data is crucial for maintaining system-wide integrity. Unauthorized modifications to code or data can lead to malfunctions, system instability, or the introduction of malicious code, compromising the hardware’s intended functionality or exploiting system vulnerabilities.
• Authentication protocols: Authentication is a fundamental and crucial component of cybersecurity, serving as the frontline defense against unauthorized access and potential security breaches. Employing authentication protocols to restrict access to authorized actors and approved software layers only using cryptography credentials is important.
• Secure software updates with rollback protection: Regular updates extend beyond bug fixes including remote firmware over-the-air (OTA) updates, guards against rollback attacks, and ensures the execution of only legitimate updates.
• Post-quantum cryptography: Anticipating the post-quantum computing era to include NIST 800-208 Leighton-Micali Signature (LMS) cryptography safeguards EVs against the potential threats posed by future quantum computers.
• Platform resiliency: Automatic detection of unauthorized code changes enables swift recovery to a secure state, effectively thwarting potential cyber threats. Adhering to NIST 800-193 recommendations for platform resiliency ensures a robust defense mechanism.
• Secure supply chain: Guaranteeing the origin and integrity of flash content throughout the supply chain, these secure flash devices prevent content tampering and misconfiguration during platform assembly, transportation, and configuration. This, in turn, safeguards against cyber adversaries.

Considering the transition to SDVs and connected cars, data vulnerability becomes even more significant.

“Depending on where data resides, different protection measures are in place,” said Keysight’s Kopacz. “Intrusion detection systems (IDS), crypto services, and key management are becoming standard solutions in vehicles. Especially sensitive data for safety features needs to be protected and verified. Thus, redundancy becomes more relevant. With SDVs, the vehicle software is constantly updated or changed throughout the entire vehicle life cycle. Ever-evolving cyber threats are particularly challenging. Accordingly, the entire vehicle software must be continuously checked for new security gaps. OEMs are going to need comprehensive testing solutions to minimize security threats. This will need to include the cybersecurity testing of the entire attack surface, covering all vehicle interfaces – wired vehicle communication networks such as CAN or automotive Ethernet or wireless connections via Wi-Fi, Bluetooth, or cellular communications. OEMs will also need to test the backend that provides over-the-air (OTA) software updates. Such solutions can reduce the risk of damage or data theft by cybercriminals.”

Data management and privacy concerns
Another issue to be resolved is how the massive amount of data collected will be managed and used. Ideally, data will be analyzed to yield commercial value without causing privacy concerns. For example, infotainment platform data might reveal what types of music are most popular, helping the music industry to improve marketing strategies. Who will monitor the transfer of such data, though? How will customers be made aware of the data collection? And will they have an opportunity to opt out of having their data sold?

As with airplanes, vehicle black boxes are installed to record information for analysis of the data after an accident occurs. The information recorded includes vehicle speed, the braking situation, and the activation of air bags, among other things. If an accident occurs resulting in a fatality, and the data from ADAS and ECU uncover vulnerability in the designs, could that data be used as evidence in court against manufacturers or their supply chains? Armed with this information, the insurance industry may decline claims. Would one or more manufacturers of the ADAS/ECU be required to hand over the data when ordered by the authorities?

“Quality requirements for sophisticated electronic parts will continue to become more rigid and strict, allowing only a few defective parts per billion (DPPB) due to the impact failed components can have on the safety and well-being of human life,” noted Guy Cortez, senior staff product management manager for SLM analytics at Synopsys. “SLM data analytics will continue to play a substantial role in the health, maintainability, and sustainability of these devices throughout their life within the vehicle. Through the power of analytics, you can do proper root cause analysis of any failed device (e.g., return merchandise authorization, or RMA). What’s more, you will also be able to find ‘like’ devices that ultimately may exhibit similar failed behavior over time. Thus empowered, you can proactively recall these like devices before they fail during operation in the field. Upon further analysis, the device(s) in question may require a design re-spin by the device developer in order to correct any identified issue. With a proper SLM solution deployed throughout the automotive ecosystem, you can achieve a higher level of predictability, and thus higher quality and safety for the automotive manufacturer and consumer.”

OEM impact
While modern cars have been described as computers on wheels, they are now more like mobile phones on wheels. OEMs are designing cars that do not skimp on features. Semi-autonomous driving, voice-controlled infotainment systems, and the monitoring of many functions—including driver behavior— are yielding a large amount of data. While that data can be used to improve future designs. OEMs’ approaches to security and privacy vary, with some offering stronger security and privacy protection than others.

Mercedes-Benz is paying attention to data security and privacy, and is compliant to UN ECE R155 / R156, a European norm for cybersecurity and software update management systems, according to the company. Which data is processed in connection with digital vehicle services depends on which services the customer selects. Only the data required for the respective service will be processed. Additionally, the “Mercedes me connect” app’s terms of use and privacy information make it transparent for customers to see what data is needed for and how it is processed. Customers can determine which services they want to use.

Hyundai indicated it would follow a user-centric focus, prioritizing safety, information security, and data privacy with fault-tolerant software architectures to enhance cybersecurity. Hyundai Motor Group’s global software center, 42dot, is currently developing integrated hardware/software security solutions that detect and block data tampering, hacking, and external cyber threats, as well as abnormal communication using big data and AI algorithms.

And according to the BMW Group, the company manages a connected fleet of more than 20 million vehicles globally. More than 6 million vehicles are updated over-the-air on a regular basis. Together with other services, more than 110 terabytes of data traffic per day are processed between the connected vehicles and cloud-backend. All BMW vehicle interfaces permit consumers to opt in or out of various types of data collection and processing that may happen on their vehicles. If preferred, BMW customers may opt out of all optional data collection relating to their vehicles at any time by visiting the BMW iDrive screen in their vehicle. Additionally, to completely stop the transfer of any data from BMW vehicles to BMW services, customers can contact the company to request that the embedded SIM on their vehicles be disabled.

Not all OEMs hold the same philosophy on privacy. According to a study on 25 brands conducted by the Mozilla Foundation, a nonprofit organization, 56% will share data with law enforcement in response to an informal request, 84% share or sell personal data, and 100% earned the foundation’s “privacy not included” warning label.

More importantly, are customers educated or informed on the privacy issue?

Fig. 2: Once data is collected from a vehicle, it can go to multiple destinations without the knowledge of customers. Source: Mozilla, *Privacy Not Included.

Applying data to automotive design in the future
OEMs collect many different types of automotive data in relation to autonomous driving, infrastructure, infotainment, connected vehicles, and vehicle health and maintenance. The ultimate goal, however, is not just to compile massive raw data; rather, it is to extract value from it. One of the questions OEMs need to ask is how to apply technology to extract information that is really useful in future automotive design.

“OEMs are trying to test and validate the various functions of their vehicles,” said David Fritz, vice president of virtual and hybrid systems at Siemens EDA. “This can involve millions of terabytes of data. Sometimes, a huge portion of the data is redundant and useless. The real value in the data is, once it gets distilled, that it’s in a form where humans can relate to the meaning of the data, and it also can be pushed into the systems while they’re being developed and tested and before the vehicles are even on the ground. We’ve known for quite some time that many countries and regulatory bodies around the world have been collecting what they call an accident database. When an accident occurs, the police show up on the scene collecting relevant data. ‘There was an intersection here, a stop sign there. And this car was traveling in this direction roughly this many miles an hour. The weather condition is this. The car entered the intersection in the yellow light and caused an accident, etc.’ This is an accident scenario. Technologies are available to take those scenarios and put them in a standard form called Open Scenario. Based on the information, a new set of data can be generated to determine what the sensors would be seeing in those accident situations, and then push it through both a virtual version of the vehicle and environment and in the future, and push those scenarios through the sensors in this physical vehicle itself. This is really the distillation of that data into a form that a human can wrap their mind around. Otherwise, you could collect billions of terabytes of raw data and try to push that into these systems, and it wouldn’t actually help you any more than if someone were sitting in a car and dragging those for billions of miles.”

But that data also can be very useful. “If an OEM wants to obtain safety certification, say in Germany, the OEM can provide a set of data of scenarios on how the vehicle will navigate,” Fritz said. “An OEM can provide a set of data to the German authority, with a set of scenarios to prove the vehicle will navigate in a safe manner under various conditions. By comparing that with the data in the accident database, the German government can say that as long as you avoid 95% of the accidents in that database, you’re certified. That’s actionable from the perspectives of human drivers, insurance, engineering, and visual simulation. The data prove the vehicle is going to behave as expected. The alternative is to drive around, as in the case of autonomous vehicles, and try to justify the accident was not caused by the vehicle, while facing the lawsuit. It does not seem to make sense, but that’s what’s happening today.”

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