Nishanth Sastry

Senior Lecturer

King's College London


Nishanth Sastry is a Senior Lecturer at King's College London, UK now a Professor of Computer Science at The University of Surrey. He holds a Bachelor's degree (with distinction) from R.V. College of Engineering, Bangalore University, a Master's degree from University of Texas, Austin, and a PhD from the University of Cambridge, all in Computer Science. Previously, he spent over six years in the Industry (Cisco Systems, India and IBM Software Group, USA) and Industrial Research Labs (IBM TJ Watson Research Center). He has been a visiting researcher at the Alan Turing Institute and Massachusetts Institute of Technology.

His honours include a Best Paper Award at SIGCOMM Mobile Edge Computing in 2017, a Best Paper Honorable Mention at WWW 2018, a Best Student Paper Award at the Computer Society of India Annual Convention, a Yunus Innovation Challenge Award at the Massachusetts Institute of Technology IDEAS Competition, a Benefactor's Scholarship from St. John's College, Cambridge, a Best Undergraduate Project Award from RV College of Engineering, a Cisco Achievement Program Award and several awards from IBM. He has been granted nine patents in the USA for work done at IBM.

Nishanth has been a keynote speaker, and received media coverage from print media such as The Times UK, New York Times, New Scientist and Nature, as well as Television media such as BBC, Al Jazeera and Sky News. He is a member of the ACM and a Senior Member of the IEEE.


  • Computer Networks and their architecture
  • Social Networks and Computational Social Science
  • Data Analytics and Machine Learning in support of the above two


  • PhD in Computer Science

    University of Cambridge

  • MA in Computer Science

    University of Texas at Austin

  • BE in Computer Science and Engineering

    R.V. College of Engineering, Bangalore University

Ongoing Projects

Full list of past and current projects is available in PURE

SynaPSE - Synthetic-trace Creation Platform for Simulating Edge Computing Scenarios

Creating probabilistic models from large real world traces, for what-if reasoning about edge networks (funded by Cisco Systems)

Social media, Smartphone use and Self-harm in Young People (3S-YP study)

Interdisciplinary project with Psychiatrists, on link between social media, phone usage and self harm (funded by MRC)

Edge for Education -- FLAME replication for educational media delivery

Advanced Media Services at the edge of the network over the FLAME platform (EC H2020 project, via FLAME)

Supporting University Curriculum & Creativity in Engineering Entrepreneurship Development (SUCCEED)

Data driven innovation through project-based learning in Cameroon (Funded by Royal Academy of Engineering).

Mobile Edge Computation in 5G

Enabling mission-critical applications like remote surgery and connected cars with edge cloud computations (funded by Vodafone)

Students and collaborators



Aravindh Raman

PDRA, working on network measurements


Animesh Chaturvedi

PDRA working on online harms and social media (to join shortly)


Damiano Di Francesco Maesa

PDRA working on distributed ledgers for 5G


Frank Sardis

Managing 5G Lab infrastructure

PhD Students


Xuehui (Rachel) Hu

PhD Student, working on third party trackers and GDPR


Pushkal Agarwal

PhD Student working with the UK Parliament on Digital Citizen Engagement


Emeka Obiodu

PhD Student, working on differentiated services for 5G


Tooba Faisal

PhD Student working with Vodafone on Service Level Agreements at the Network Edge


Abdullahi Abubakar

PhD Student working on sharing economy applications over edge networks for developing regions



Miriam Redi

Visiting Researcher, Wikimedia Research



Sagar Joglekar

Former PhD student (now Research Scientist at Bell Labs Cambridge)


Dmytro Karamshuk

Former Postdoc, now Research Scientist at Facebook Core Data Science.


Peter Young

Former Postdoc (now Data Scientist at Accuity)


Changtao Zhong

Former PhD student (now Data Scientist at Twitter)

Recent Publications

Quickly discover relevant content by filtering publications.

Multi-country Study of Third Party Trackers from Real Browser Histories

This paper aims to understand how third-party ecosystems have developed in four different countries: UK, China, AU, US. We are interested in how wide a view a given third-party player may have, of an individual user's browsing history over a period of time, and of the collective browsing histories of a cohort of users in each of these countries. We study this by utilizing two complementary approaches: the first uses lists of the most popular websites per country, as determined by Alexa.com. The second approach is based on the real browsing histories of a cohort of users in these countries. Our larger continuous user data collection spans over a year. Some universal patterns are seen, such as more third parties on more popular websites, and a specialization among trackers, with some trackers present in some categories of websites but not others. However, our study reveals several unexpected country-specific patterns: China has a home-grown ecosystem of third-party operators in contrast with the UK, whose trackers are dominated by players hosted in the US. UK trackers are more location sensitive than Chinese trackers. One important consequence of these is that users in China are tracked lesser than users in the UK. Our unique access to the browsing patterns of a panel of users provides a realistic insight into third party exposure, and suggests that studies which rely solely on tt Alexa top ranked websites may be over estimating the power of third parties, since real users also access several niche interest sites with lesser numbers of many kinds of third parties, especially advertisers.

What a Tangled Web We Weave: Understanding the Interconnectedness of the Third Party Cookie Ecosystem

When users browse to a so-called First Party website, other third parties are able to place cookies on the users’ browsers. Although this practice can enable some important use cases, in practice, these third party cookies also allow trackers to identify that a user has visited two or more first parties which both share the second party. This simple feature been used to bootstrap an extensive tracking ecosystem that can severely compromise user privacy. In this paper, we develop a metric called tangle factor that measures how a set of first party websites may be interconnected or tangled with each other based on the common third parties used. Our insight is that the interconnectedness can be calculated as the chromatic number of a graph where the first party sites are the nodes, and edges are induced based on shared third parties. We use this technique to measure the interconnectedness of the browsing patterns of over 100 users in 25 different countries, through a Chrome browser plugin which we have deployed. The users of our plugin consist of a small carefully selected set of 15 test users in UK and China, and 1000+ in-the-wild users, of whom 124 have shared data with us. We show that different countries have different levels of interconnectedness, for example China has a lower tangle factor than the UK. We also show that when visiting the same sets of websites from China, the tangle factor is smaller, due to blocking of major operators like Google and Facebook. We show that selectively removing the largest trackers is a very effective way of decreasing the interconnectedness of third party websites. We then consider blocking practices employed by privacy-conscious users (such as ad blockers) as well as those enabled by default by Chrome and Firefox, and compare their effectiveness using the tangle factor metric we have defined. Our results help quantify for the first time the extent to which one ad blocker is more effective than others, and how Firefox defaults also greatly help decrease third party tracking compared to Chrome.

Characterising User Content on a Multi-lingual Social Network

Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multi-cultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.

Is it time for a 999-like (or 112/911) system for critical information services?

The nature of information gathering and dissemination has changed dramatically over the past 20 years as traditional media sources are increasingly being replaced by a cacaphony of social media channels. Despite this, society still expects to disseminate its critical information via traditional news sources. Public Warning Systems (PWS) exist, but concerns about spamming users with irrelevant warnings mean that mostly only life threatening emergency warnings are delivered via PWS. We argue that it is time for society to upgrade its infrastructure for critical information services (CIS) and that a smartphone app system can provide a standardised, less-intrusive user interface to deliver CIS, especially if the traffic for the app is prioritised during congestion periods. Accordingly, we make three contributions in this paper. Firstly, using network parameters from our longitudinal measurements of network performance in Central London (an area of high user traffic), we show, with simulations, that reserving some bandwidth exclusively for CIS could assure QoS for CIS without significant degradation for other services. Secondly, we provide a conceptual design of a 999 CIS app, which can mimic the current 999 voice system and can be built using 3GPP defined systems. Thirdly, we identify the stakeholder relationships with industry partners and policymakers that can help to deliver a CIS system that is fit for purpose for an increasingly smartphone-based society.

PAIGE: Towards a Hybrid-Edge Design for Privacy-Preserving Intelligent Personal Assistants

Intelligent Personal Assistants (IPAs) such as Apple's Siri, Google Now, and Amazon Alexa are becoming an increasingly important class of web-service application. In contrast to keyword-oriented web search, IPAs provide a rich query interface that enables user interaction through images, audio, and natural language queries. However, supporting this interface involves compute-intensive machine-learning inference. To achieve acceptable performance, ML-driven IPAs increasingly depend on specialized hardware accelerators (eg GPUs, FPGAs or TPUs), increasing costs for IPA service providers. For end-users, IPAs also present considerable privacy risks given the sensitive nature of the data they capture. In this paper, we present Privacy Preserving Intelligent Personal Assistant at the EdGE (PAIGE), a hybrid edge-cloud architecture for privacy-preserving Intelligent Personal Assistants. PAIGE's design is founded on the assumption that recent advances in low-cost hardware for machine-learning inference offer an opportunity to offload compute-intensive IPA ML tasks to the network edge. To allow privacy-preserving access to large IPA databases for less compute-intensive pre-processed queries, PAIGE leverages trusted execution environments at the server side. PAIGE's hybrid design allows privacy-preserving hardware acceleration of compute-intensive tasks, while avoiding the need to move potentially large IPA question-answering databases to the edge. As a step towards realising PAIGE, we present a first systematic performance evaluation of existing edge accelerator hardware platforms for a subset of IPA workloads, and show they offer a competitive alternative to existing datacenter alternatives.