Objectives To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy. Design Cross sectional study Setting Health related apps developed for the Android mobile platform, available in the Google Play store in Australia and belonging to the medical and health and fitness categories. Participants Users of 20 991 mHealth apps (8074 medical and 12 917 health and fitness found in the Google Play store: in-depth analysis was done on 15 838 apps that did not require a download or subscription fee compared with 8468 baseline non-mHealth apps. Main outcome measures Primary outcomes were characterisation of the data collection operations in the apps code and of the data transmissions in the apps traffic; analysis of the primary recipients for each type of user data; presence of adverts and trackers in the app traffic; audit of the app privacy policy and compliance of the privacy conduct with the policy; and analysis of complaints in negative app reviews. Results 88.0% (n=18 472) of mHealth apps included code that could potentially collect user data. 3.9% (n=616) of apps transmitted user information in their traffic. Most data collection operations in apps code and data transmissions in apps traffic involved external service providers (third parties). The top 50 third parties were responsible for most of the data collection operations in app code and data transmissions in app traffic (68.0% (2140), collectively). 23.0% (724) of user data transmissions occurred on insecure communication protocols. 28.1% (5903) of apps provided no privacy policies, whereas 47.0% (1479) of user data transmissions complied with the privacy policy. 1.3% (3609) of user reviews raised concerns about privacy. Conclusions This analysis found serious problems with privacy and inconsistent privacy practices in mHealth apps. Clinicians should be aware of these and articulate them to patients when determining the benefits and risks of mHealth apps.

Abstract-The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDNbased management architectures, it is of paramount importance to design a monitoring system that can provide frequent and consistent updates to heterogeneous management applications. For the monitoring functionality to scale according to the requirements of large-scale networks a distributed monitoring approach is required. In this paper we present a decentralized approach for resource monitoring in SDN, which is designed to support a wide range of measurement tasks and requirements in terms of monitoring rates and information granularity levels. Our solution leverages effective processing of the monitoring requests to reduce the consumption of limited resources, such as the control plane bandwidth of OpenFlow switches. To demonstrate the benefits of the proposed approach, our evaluation is based on a realistic and demanding use case, where a distributed management application coordinates a content distribution service in an ISP network.

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDNbased management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.

Abstract-Network operators have recently been developing multi-Gbps traffic monitoring tools that execute on commodity hardware and are part of the packet-processing pipelines realizing software dataplanes. These solutions allow sophisticated tasks to be performed on a per-packet basis, without relying on sampling or passive trace analysis, by leveraging the processing power available on servers. Although advances in packet capture have enabled intercepting packets from network cards at high rates, bottlenecks can still arise in the monitoring process as a result of concurrent access to shared processor resources, variations of the traffic skew, and unbalanced packet-rate spikes. In this paper we present an adaptive traffic monitoring approach that copes with emerging bottlenecks by timely detecting changes in the operational conditions and reconfiguring monitoring-related operations for subsets of traffic flows. Our solution performs responsive adaptations at the time scale of milliseconds and does not require a significant amount of resources. To demonstrate the capabilities of our approach we implemented it as part of a generic packet-processing pipeline and show that lossless traffic monitoring can be achieved for a wide range of conditions.

Objective We conduct a first large-scale analysis of mobile health (mHealth) apps available on Google Play with the goal of providing a comprehensive view of mHealth apps’ security features and gauging the associated risks for mHealth users and their data. Materials and Methods We designed an app collection platform that discovered and downloaded more than 20 000 mHealth apps from the Medical and Health & Fitness categories on Google Play. We performed a suite of app code and traffic measurements to highlight a range of app security flaws: certificate security, sensitive or unnecessary permission requests, malware presence, communication security, and security-related concerns raised in user reviews. Results Compared to baseline non-mHealth apps, mHealth apps generally adopt more reliable signing mechanisms and request fewer dangerous permissions. However, significant fractions of mHealth apps expose users to serious security risks. Specifically, 1.8% of mHealth apps package suspicious codes (eg, trojans), 45.0% rely on unencrypted communication, and as much as 23.0% of personal data (eg, location information and passwords) is sent on unsecured traffic. An analysis of the app reviews reveals that mHealth app users are largely unaware of the surfaced security issues. Conclusion Despite being better aligned with security best practices than non-mHealth apps, mHealth apps are still far from ensuring robust security guarantees. App users, clinicians, technology developers, and policy makers alike should be cognizant of the uncovered security issues and weigh them carefully against the benefits of mHealth apps.