The rise of Privacy Enhancing Technologies (PETs) has witnessed tremendous growth in recent years. A recent research publication from MIT  has placed their annual growth at 178% compared to other technologies. Although PETs have found early adopters in a few large corporations, academia or government institutions, their mainstream adoption could spur access to data among Business to Business enterprises.
The awareness of end users on data privacy and the expansion of regional regulation on data protections could lead to more adoption. However, like most emerging technologies, PETs have inherent challenges affecting its maturity and most notably in the IoT space. The following sections discuss some of these problems as gleaned from G. M. Garrido et al.’s “Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review” study . To learn more about privacy techniques in the future, please follow Callis Ezenwaka and OpenMined on twitter.
Classification of PETs Shortcomings
These challenges can be fanned-out into two categories namely the narrow challenges and the broad challenges each having distinct sub-categories as shown in the figure 1 below:
- The narrow challenges consists of (i) the trade-off between utility and privacy, (ii) the Recursive Enforcement Problem (REP) and (iii) the Copy Problem (CP)
- The broad challenges comprises (i) the IoT impact on privacy, (ii) the attacks on privacy and (iii) the Legal challenges.
The Narrow Challenges
The Utility and Privacy trade-off: The dilemma between preserving the privacy of data producers and deriving useful insight from released data by consumers has posed a considerable challenge for practitioners . The need to validate data authenticity has further widened these differences - as producers strive to maintain maximum privacy, data consumers demand maximum utility thus increasing the complexity of the trade-off . While PETs ensure plausible deniability, they inadvertently reduce data authenticity and accountability.
Contextually, this could be the desired benefit while at other times like during a health emergency, it could be a barrier for such activities like contact tracing. In order to strike an optimal balance between the trade-off, practitioners should clearly define the accountability threshold and set up deterrents for parties that contravene such. Appropriate measures like outright ban could be adopted to deter participants from peddling unauthentic data.
The recursive enforcement problem (REP): The existence of trust relationship between a data producer (the trustor) and a data consumer (the trustee)  is based on the principle that the trustee will always act within agreed threshold without further supervision. However, this is not always the case as evidenced in the increasing number of data breaches. The issue of trust  and cost  of third party supervision has significantly slowed the advancement of the IoT data market.
The recursive enforcement problem manifests itself in a multi-layered supervision structure. A trustee needs a gatekeeper A to enforce proper oversight, then this gatekeeper A needs another authority gatekeeper B, who in turn needs another authority gatekeeper C and so the whole process cascades into an onion-like form. Using PETs like differential privacy and homomorphic encryption could reduce the need for such third party supervision and ultimately reduce single point of failure and contrived trust - i.e. accepting the sometimes poor privacy conditions in a monopoly.
The Copy Problem (CP): The idea of perceiving data as a tradable asset could create an aversion for data owners to share them. The fear of potentially forgoing the benefits derived from shared data or releasing information that could be potentially used against someone reinforces such aversion. Copy Problem could impose a barrier, both in the industry and among customers, for data owners to engage in data markets.
The notion that shared data would not preserve its value as soon as it is released and the inability of data owners to allow a non-agreed computation could potentially make original data scarce. In order to make access to data more appealing and attractive to data producers, secure and outsourced computation could be adopted. The use of PETs such as trusted execution environments or homomorphic encryption can tackle the CP by allowing other entities to extract value without ever losing control over the input data beyond the specific information sold.
The Broad Challenges
The IoT impact on privacy: The advent of IoT brought about ubiquitous and proliferation of large amounts of data. The effect of data generated by these devices could be seen in digital transformation across both the legacy industries like manufacturing and in other consumer products and services.
However, IoT devices have inherent shortcomings that could affect privacy. Since privacy could be constrained by such factors like context and employed technologies, underscores the need for strict adherence to privacy-by-design principles . Therefore, a call for practitioners working within the domain to pull resources in order to tackle the diverse issues that IoT devices entail for privacy.
The table below contains an overview of these shortcomings and briefly highlights some of their impact on privacy.
Table 1: Explaining the issues that the IoT will bring in the domain of privacy.
The attacks on privacy: Agents could establish malicious attacks on users’ privacy with the intention of data re-identification or to reveal hidden insights on the collected information. Such adversary attacks within the context of the IoT assume various forms and are extensively prevalent . Furthermore, other attack vectors have been discussed  including data forwarding, roles collision, and side channel attacks.
The benefits which IoT devices could bring to the data markets such as facilitation of access to large quantities of data from different domains could be offset by the impact of increasing number of these attacks and the potential harms they portend to individuals. Hence, IoT data markets may require more robust security standards and adoption of PETs such as Differential Privacy should be considered.
Legal challenges: The past decades has witnessed an increasing number of regulations that addressed the right to data and the ability of both individuals and businesses to commercially exploit their data. These extant laws have inadvertently uncovered the untapped potential of data for innovations and have created a leeway for data markets to thrive . Unfortunately, the inability to realistically monitor online privacy violations, coupled with the shrewdness of adversary agents and the existence of illegal shadow markets portrays these laws as reactionary rather than proactive measures.
This sets off a conundrum as more stringent privacy regulation could stifle free markets and innovations . A number of mitigation steps that address these challenges could be explored such as the standardization of data market and IoT devices. Also, compliance and the enforcement of regional legislation such as GDPR  could fast track the maturity of both PETs, IoT devices and data markets.
There is the need to optimally balance the trade-off between data authenticity and privacy in order for data markets to thrive. The maturity of PETs could help reduce the recursive enforcement problem and the copy problem. An approach like privacy-enhancing verification could improve trust among practitioners leading to more accountability.
Additionally, the current constraints that affect IoT devices, particularly bring a new set of challenges for privacy enhancement. The adoption of PETs such as differential privacy and homomorphic encryption for IoT devices could reduce the attack surface for malicious agents and ultimately impact data markets positively. More robust legislation that determines the sovereignty layer in data market design should be pursued. This is because the ownership and management rules for the participants’ data impact the PETs selection for the rest of the layers.
Special thanks to Abinav Ravi for thorough editing and Gonzalo Munilla Garrido for holistic review of this article.
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