Forrester Research just published The Top 10 Technology Trends To Watch: 2018 To 2020. Ten trends, which Forrester breaks into three phases of dawning, awareness, and acceptance, are setting the pace of technology-driven business change. In the dawning phase, a few innovators experiment with new technology-enabled business models and exploit emerging technologies. In the awareness phase, change agents leverage the accelerating returns of evolving technology to steal customers, improve the bottom line, and inflict massive impacts on industries. In the acceptance phase, surviving enterprises finally make the tough changes necessary to fight disruptors.

Forrester Research just published The Top 10 Technology Trends To Watch: 2018 To 2020. Ten trends, which Forrester breaks into three phases of dawning, awareness, and acceptance, are setting the pace of technology-driven business change. In the dawning phase, a few innovators experiment with new technology-enabled business models and exploit emerging technologies. In the awareness phase, change agents leverage the accelerating returns of evolving technology to steal customers, improve the bottom line, and inflict massive impacts on industries. In the acceptance phase, surviving enterprises finally make the tough changes necessary to fight disruptors.

Artificial intelligence will continue to dominate technology investments in 2018, along with cloud computing, the Internet of Things and customer-focused applications. In their new report, "The Top 10 Technology Trends to Watch: 2018 To 2020 - Ten Trends Will Help You Maximize the Value of Business Technology," Forrester Research analysts Brian Hopkins, Bobby Cameron, Ted Schadler and Rusty Warner offer their picks on the technology and business trends that will most shape the IT landscape. "The growth of IoT aspirations and technologies has led to a host of technology innovations in edge devices, such as gateway servers, microdata centers, cloudlets, fog fabric nodes, intelligent routers and device firmware," the authors write. "Firms in the vanguard of this trend will engage customers more quickly and squeeze new efficiencies out of processes. Exploiting computing power on the edge will give them an actual edge. CIOs must understand the extension of compute to the edge to find opportunities for competitive advantage."

In trust systems, unfair rating attacks — where advisors provide ratings dishonestly — influence the accuracy of trust evaluation. A secure trust system should function properly under all possible unfair rating attacks; including dynamic attacks. In the literature, camouflage attacks are the most studied dynamic attacks. But an open question is whether more harmful dynamic attacks exist. We propose random processes to model and measure dynamic attacks. The harm of an attack is influenced by a user's ability to learn from the past. We consider three types of users: blind users, aware users, and general users. We found for all the three types, camouflage attacks are far from the most harmful. We identified the most harmful attacks, under which we found the ratings may still be useful to users.

Trust systems are widely used to facilitate interactions among agents based on trust evaluation. These systems may have robustness issues, that is, they are affected by various attacks. Designers of trust systems propose methods to defend against these attacks. However, they typically verify the robustness of their defense mechanisms (or trust models) only under specific attacks. This raises problems: first, the robustness of their models is not guaranteed as they do not consider all attacks. Second, the comparison between two trust models depends on the choice of specific attacks, introducing bias. We propose to quantify the strength of attacks, and to quantify the robustness of trust systems based on the strength of the attacks it can resist.Our quantification is based on information theory, and provides designers of trust systems a fair measurement of the robustness.

Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] — based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance — specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.

Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between strangers from trust ratings between acquaintances and the structure of the network that connects them. We investigate a trust propagation algorithm that is based on user triads where the trust one user has in another is predicted based on an intermediary user. The propagation function can be applied iteratively to propagate trust along paths between a source user and a target user. We evaluate this approach using the trust network of the CouchSurfing community, which consists of 7.6M trust-valued edges between 1.1M users. We show that our model out-performs one that relies only on the trustworthiness of the target user (a kind of public trust system). In addition, we show that performance is significantly improved by bringing in user-level variability using mixed-effects regression models.