Internet of Things (IoT) devices generate, store, and exchange large volumes of potentially sensitive information. This information may relate to personal behaviour, healthcare, transportation, industrial operations, energy consumption, or the activities taking place within homes and workplaces.

Conventional privacy mechanisms such as encryption, anonymisation, access control, and privacy-preserving routing remain important. However, IoT privacy also involves strategic decisions made by users, attackers, service providers, device owners, and network defenders.

This blog post discusses our survey paper, Game-Theoretic Analytics for Privacy Preservation in Internet of Things Networks https://doi.org/10.1016/j.engappai.2024.108449, published in Engineering Applications of Artificial Intelligence.

Why use game theory for IoT privacy?

Privacy protection often involves participants with different and sometimes conflicting objectives. A user may wish to obtain a service while revealing as little personal information as possible. A service provider may need sufficient data to operate effectively. An attacker may attempt to infer private information, while a defender must decide how and when to intervene.

These interactions cannot always be represented adequately as isolated technical problems. The decision made by one participant may depend on the expected actions of others.

Game theory provides mathematical tools for representing these strategic relationships. It can help researchers identify the participants, available strategies, costs, rewards, information assumptions, and possible outcomes associated with a privacy problem.

What does the survey cover?

The survey explains several forms of game theory that have been applied to IoT privacy preservation, including:

• simultaneous games;
• stochastic games;
• bargaining games;
• differential games;
• mean field games;
• aggregation games;
• Stackelberg games;
• signalling games;
• repeated games;
• evolutionary games; and
• cooperative games.

These game types make different assumptions about timing, information, cooperation, system evolution, and the relationships between participants.

For example, a Stackelberg game can represent a leader who acts first and a follower who responds. A signalling game can represent interactions in which one participant has private information. A stochastic game can represent repeated privacy decisions in a system whose state changes over time.

Which IoT environments were considered?

We reviewed game-theoretic privacy-preservation research across a range of IoT applications, including:

1. Smart grids.
2. Intelligent transportation systems.
3. Crowdsensing platforms.
4. Edge-based IoT environments.
5. Integrated energy systems.
6. Blockchain-enabled IoT.
7. Social IoT.
8. Industrial IoT.

The survey compares how different games have been used to address privacy issues within these environments. It also considers the assumptions, strengths, and limitations of the proposed approaches.

What are the wider lessons?

There is no single game-theoretic model suitable for every IoT privacy problem. The model must reflect how participants interact, what information they possess, whether they cooperate, and how the environment changes.

A model that is mathematically elegant may still be unsuitable if its assumptions do not match the real system. For example, complete information about attackers and defenders may not be available in a deployed IoT network.

The study therefore highlights the importance of connecting theoretical models with realistic IoT architectures, threat models, datasets, and operational constraints.

Future research directions

The survey identifies several opportunities for further research. These include modelling incomplete information, supporting large and heterogeneous IoT environments, integrating game theory with artificial intelligence, and developing privacy mechanisms that adapt as user behaviour and threat conditions change.

There is also a need for greater experimental validation. Game-theoretic privacy mechanisms should be evaluated using realistic testbeds, operational datasets, and application-specific measures of privacy and system utility.

Perspective

Game theory offers more than a collection of mathematical models. It provides a way of thinking about privacy as a strategic interaction between participants whose objectives may not align.

This perspective is valuable because privacy decisions rarely exist in isolation. Users, devices, organisations, service providers, and attackers continually affect one another’s choices. Understanding these interactions can improve the design of privacy-preserving IoT systems.

The paper was co-authored with Yizhou Shen, Carlton Shepherd, Shigen Shen, Xiaoping Wu, Wenlong Ke, and Shui Yu.

Research collaboration and consultancy

Our work supports organisations seeking to understand or design privacy-preserving IoT systems using game theory, artificial intelligence, and adaptive decision-making.

We welcome enquiries relating to:

• privacy threat modelling for IoT systems;
• game-theoretic security and privacy analysis;
• privacy preservation in smart grids and smart cities;
• industrial IoT and critical-infrastructure privacy;
• edge computing and data-sharing risks;
• intelligent transportation and crowdsensing privacy;
• blockchain-enabled IoT;
• responsible and privacy