Christina Giannoula will be joining the tenure-track faculty at our institute starting January 2026. She will lead the SPIN research group, and is actively seeking motivated students and researchers to join her team.

Christina's research interests lie at the intersection of computer architecture, computer systems, high-performance computing, and sustainable computing. Her current research focuses on the hardware/software co-design of emerging applications, particularly AI/ML, with modern computing systems. She designs solutions across the entire system stack, from software down to hardware—including algorithms, compilers, runtime systems, programming frameworks, and hardware engines—leveraging cutting-edge technologies such as processing-in-memory and memory disaggregation. Her work targets improvements in performance, scalability, programmability, and sustainability.

Prior to joining MPI-SWS, Christina was a Postdoctoral Researcher at the University of Toronto, where she received several research distinctions, including Postdoctoral Research Awards from the Vector Institute for Artificial Intelligence. She was selected as a 2024 MLSys Rising Star and 2024 EECS Rising Star. She received her Ph.D. from the School of Electrical and Computer Engineering (ECE) at the National Technical University of Athens (NTUA) in Greece, where she was a member of the Computing Systems Laboratory. Her Ph.D. thesis received the 2022 Iakovos Gurounian Award for the Ph.D. thesis with the highest industrial impact.

 

Paul Francis's Open Diffix project has released SynDiffix, an open-source Python package that generates statistically accurate, privacy preserving synthetic data from structured data. Using SynDiffix, a data owner can safely share data while retaining most of the statistical properties of the original data. Analysts can work with the synthetic data as though it were the original.

Using the novel techniques of sticky noise and range snapping, SynDiffix breaks new ground in data accuracy. It is 10 to 100 times more accurate than the open source tool CTGAN, and 5 to 10 times more accurate than the best commercial synthetic data generators. This makes SynDiffix particularly well suited to descriptive analytics like histograms, heatmaps, averages and standard deviations, column correlations, and so on. Like other tools, however, it can also be used for ML modeling. Francis is hopeful that SynDiffix will find wide practical use as well as motivate more research on synthetic data.

Pramod Bhatotia, who completed his doctoral studies at MPI-SWS, has received the 2023 EuroSys Jochen Liedtke Young Researcher Award.

The EuroSys Jochen Liedtke Young Researcher Award was created in 2014 by ACM EuroSys to reward junior European researchers who have demonstrated exceptional creativity and innovation in systems research, broadly construed. The award is given annually at the EuroSys conference, in memory of Jochen and his fundamental contributions to the systems community. The award is accompanied by a 2,000 EUR cash prize generously provided by RedHat.

Congratulations, Pramod!

Two MPI-SWS faculty, Maria Christakis and Elissa Redmiles, have earned highly competitive Google Research Scholar awards. Maria Christakis's award was given for her research on metamorphic specification and testing of machine-learning models and Elissa Redmiles's award was given for her research on aligning technical data privacy protections with user concerns.

The Google Research Scholar Program provides unrestricted gifts of up to $60,000 to support research at institutions around the world and is focused on funding world-class research conducted by early-career professors. Award proposals go through an internal, merit-based review process and selected faculty can receive a Google Research Scholar award only once in their career. Award recipients are assigned a liaison at the company to share findings with and as a point of contact for further collaboration.

Open Diffix is an MPI-SWS-supported open software project to develop strong but usable data anonymization. Open Diffix is built on the data anonymization technology Diffix, which has been developed in a research partnership between the group of Paul Francis and Aircloak GmbH. Open Diffix recently released its first tool, "Diffix for Desktop". Designed with extreme ease-of-use in mind, Diffix for Desktop can be used by non-experts to safely release sensitive data to the public. Diffix for Desktop frees users from concerns about anonymity, allowing them to focus on data quality.

Antoine Kaufmann has joined the tenure-track faculty at our institute, effective October 1, 2021.  He has been a member of our faculty as a research group leader since joining MPI-SWS in August 2018.  Antoine's research centers on the interplay of software and hardware in modern systems.  He is interested in the nascent challenges in designing, implementing, and maintaining hardware-software systems for different application domains, starting with data center networking and machine learning.

Prior to joining MPI-SWS, Antoine received his Ph.D. from the University of Washington, and his Master's and Bachelor's degrees from ETH Zurich.

MPI-SWS faculty member Elissa Redmiles, along with collaborators Samuel Dooley and Professor John Dickerson from the University of Maryland as well as Professor Dana Turjeman from Reichman University, helped the Louisiana Department of Health advertise their COVID19 contact tracing app. As part of this work, the researchers conducted a randomized, controlled field experiment to provide guidance to other jurisdictions on how to most effectively and ethically advertise these public health tools.The Linux Public Health Foundation has featured their findings as guidance for other jurisdictions looking to advertise their contact tracing apps.

This work is part of a larger project Redmiles leads on ethical adoption of COVID 19 apps: https://covidadoptionproject.mpi-sws.org/.

MPI-SWS faculty member Elissa Redmiles was quoted in Rolling Stone, El Pais -- Spain's largest newspaper -- and in NetzPolitik regarding ongoing research in collaboration with MPI-SWS group member Vaughn Hamilton and MPI-SWS Intern Hanna Barakat (also at Brown University) on the shift toward digital sex work as a result of COVID19, as well as her work with collaborators Catherine Barwulor (Clemson University), Allison McDonald (University of Michigan), and Eszter Hargittai (University of Zurich) on digital discrimination against European sex workers, originally published in ACM CHI.

Ralf Jung's doctoral dissertation on "Understanding and Evolving the Rust Programming Language" has received the ACM Doctoral Dissertation Award Honorable Mention. The ACM Doctoral Dissertation Award is considered to be one of the most prestigious international dissertation awards in the area of computer science, and there are only two Honorable Mentions given for the award each year. The Honorable Mention Award comes with a prize of $10,000 and an invitation to accept the award at the annual ACM Awards Banquet in San Francisco.

Ralf's work has previously received the ETAPS Doctoral Dissertation Award and the Otto Hahn Medal, as well as being featured in the April 2021 issue of Communications of the ACM in an article entitled "Safe Systems Programming in Rust". For more details see the Saarland Informatics Campus press release.

Aastha Mehta, a doctoral student in the Distributed Systems group and the Security & Privacy group, has accepted a position as a tenure-track assistant professor in the Department of Computer Science at University of British Columbia, Vancouver, Canada. Congratulations Aastha!

Aastha's research interests span systems security, data privacy, operating systems, and distributed systems. She has worked on building systems for ensuring policy compliance and for mitigating network side-channel leaks in online services. You can find out more about her work at https://people.mpi-sws.org/~aasthakm/.

On January 9, 2020, MPI-SWS faculty member Paul Francis published the article Dear Differential Privacy: Put Up or Shut Up, on Medium. The article, which has now reached 1000 views, describes the failure of Differential Privacy as the basis for data protection in the Facebook / Social Sciences One project.

The Facebook / Social Sciences One project is an attempt to release Facebook data on URL sharing to researchers so as to better understand the role of Facebook in influencing elections. The project raised 11 million dollars from private funders, and research grants were awarded to twelve research teams around the world. Facebook decided to use Differential Privacy as the means of anonymizing the data. After one year, however, Facebook had not supplied the data. When the funders threatened to pull the funding, Facebook did release a dataset, but the quality of the data was so poor that the proposed research could not be done.

Francis' article describes how and why the data release failed, discusses the shortcomings of Differential Privacy, and calls on the privacy research community to expand the scope of what passes for valid data anonymity research.

The paper "ERIM: Secure, Efficient, In-process Isolation with Memory Protection Keys (MPK)" received a Distinguished Paper Award at the 2019 Usenix Security Symposium. It was selected as one of 6 distinguished papers out of 113 papers that appeared in the conference proceedings.

The work was also selected as the recipient of the Usenix Internet Defense Prize, along with a USD 100k gift from Facebook to support  further development of the technology.

The paper was authored by MPI-SWS doctoral students Anjo Vahldiek-Oberwagner, Eslam Elnikety, and Michael Sammler, along with MPI-SWS intern Nuno Duarte and MPI-SWS faculty members Deepak Garg and Peter Druschel.

Read more about ERIM here.

Keon Jang has joined the institute as a tenure-track faculty member, effective February 1, 2019. Keon Jang is joining us from Google, where he has been a software engineer since 2016. He is broadly interested in network systems and currently his work focuses on network performance isolation in data-center networks.

Prior to Google, he worked on software support for on network function virtualization (NFV) at Intel Labs. He received his PhD in Computer Science from KAIST, and subsequently held a postdoctoral research position at Microsoft Research Cambridge, UK.

Consider the everyday websites and apps that we use: online shops, news websites, search engines, social networks, navigation apps, instant messaging apps, and many more.  Most of these programs don't just run in isolation on our laptops or phones, but instead connect over the internet to backends and databases running in datacenters across the world.  These backends perform a wide range of tasks, including constructing your personalized social network feed, storing and retrieving comments on message boards, and calculating results for your search query.  From our perspective as users, the actions we perform are simple, such as opening the app and loading our personalized profile.  But under the hood, each action usually results in complex processing across many processes and machines in a datacenter.

It has never been easier to write and deploy complex programs like these.  Cloud computing companies who own datacenters (such as Google, Amazon, and Microsoft) will gladly rent out computer services at a touch of a button, on demand.  Using designs like microservices, it is easy for programmers to construct complex programs out of smaller, simpler building blocks.  There are frameworks and open-source software packages to help developers construct big applications out of small pieces, to spread those pieces out over multiple machines in a datacenter, and to have the pieces communicate and interact with each other over the network.

Problems show up when software goes live.  Compared to developing and deploying the software, it is much harder to make sure everything goes smoothly when the software is up and running.  Distributed computer programs have lots of moving pieces, and there are lots of opportunities for things to go wrong.  For example, if one machine in the datacenter has a hardware problem, or the code is buggy, or too many people are trying to access it at once, the effects can be wide-ranging.  It can create a butterfly effect of problems, which we term cascading failures, that can lead to the app or website as a whole becoming slow, or going down entirely.  It's hard for programmers to get to the bottom of these kinds of problems, because there's no single machine or process doing all the work.  A problem that occurs on one machine might manifest as strange symptoms on a different machine later on.  Figuring out the root cause of a problem is challenging, as is anticipating problems in the first place.  Even big internet companies like Facebook and Google experience problems like this today.

These kinds of problems motivate the research of the Cloud Software Systems Research Group at the Max Planck Institute for Software Systems.  We research ways for operators to understand what's going on in their live distributed system, to troubleshoot problems when they occur at runtime, and to design systems that proactively avoid problems.  One approach we take is to design distributed tracing tools that can be used by the system operators.  The goal of distributed tracing is to record information about what a program does while it's running.  The tools record events, metrics, and performance counters, which together expose the current state and performance of the system, and how it changes over time.  A key additional step taken by distributed tracing tools is to record the causal ordering of events happening in the system — that is, the interactions and dependencies between machines and processes.  Causal ordering is is very useful for diagnosing problems that span multiple processes and machines, especially when there might be lots of concurrent, unrelated activity going on at the same time.  It lets us reconstruct the end-to-end execution paths of requests, across all components and machines, and then reason about the sequence of conditions and events that led up to a problem.  Without causal ordering, this information is missing, and pinpointing the root cause of a problem would be like searching for a needle in a haystack.

The Cloud Software Systems Research Group has looked at a number of challenges in making distributed tracing tools efficient, scalable, and more widely deployable. In our recent work, we have thought about how you can efficiently insert instrumentation to record entirely new information, into an already-running system, without having to rebuild or restart the system [1].  We have looked at problems in dealing with the large volume of data generated by distributed tracing tools, and deciding which data is most valuable to keep if there's not enough room to keep it all [2].  We have also considered the implications of distributed tracing at extremely large scale, and how to efficiently collect, aggregate, and process tracing data in real-time [3].

In our ongoing work, we are investigating ways for the data recorded by tracing tools to feed back in to decisions made by datacenter infrastructure, such as resource management, scheduling, and load balancing.  We are also considering new challenges that arise in scalable data analysis: how do you analyze large datasets of traces and derive insights about aggregate system behavior?  One approach we are exploring uses techniques in representational machine learning, to transform richly annotated tracing data into a more tractable form for interactive analysis.  More broadly, our group investigates a variety of approaches besides just distributed tracing tools, including ways to better design and develop the distributed systems in the first place.  Ultimately, our goal is to make modern cloud systems easier to operate, understand, and diagnose.

References

[1] Jonathan Mace, Ryan Roelke, and Rodrigo Fonseca.  Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems.  In Proceedings of the 25th ACM Symposium on Operating Systems Principles (SOSP '15), 2015.

[2] Pedro Las-Casas, Jonathan Mace, Dorgival Guedes, and Rodrigo Fonseca.  Weighted Sampling of Execution Traces: Capturing More Needles and Less Hay.  In Proceedings of the 9th ACM Symposium on Cloud Computing (SoCC'18), 2018.

[3] Jonathan Kaldor, Jonathan Mace, Michał Bejda, Edison Gao, Wiktor Kuropatwa, Joe O'Neill, Kian Win Ong, Bill Schaller, Pingjia Shan, Brendan Viscomi, Vinod Venkataraman, Kaushik Veeraraghavan, Yee Jiun Song.  Canopy: An End-to-End Performance Tracing And Analysis System. In Proceedings of the 26th ACM Symposium on Operating Systems Principles (SOSP '17), 2017.

A long-standing problem both within research and in society generally is that of how to analyze data about people without risking the privacy of those people. There is an ever-growing amount of data about people: medical, financial, social, government, geo-location, etc. This data is very valuable in terms of better understanding ourselves. Unfortunately, analyzing the data in its raw form carries the risk of exposing private information about people.

The problem of how to analyze data while protecting privacy has been around for more than 40 years---ever since the first data processing systems were developed. Most workable solutions are ad hoc: practitioners try things like removing personally identifying information (e.g. names and addresses), aggregating data, removing outlying data, and even swapping some data between individuals. This process can work reasonably well, but it is time-consuming, requires substantial expertise to get right, and invariably limits the accuracy of the analysis or the types of analysis that can be done.

A holy grail within computer science is to come up with an anonymization system that has formal guarantees of anonymity and provides good analytics. Most effort in this direction has focused on two ideas, K-anonymity and Differential Privacy. Both can provide strong anonymity, but except in rare cases neither can do so while still providing adequate analytics. As a result, common practice is still to use informal ad hoc techniques with weak anonymization, and to mitigate risk by for instance sharing data only with trusted partners.

The European Union has raised the stakes with the General Data Protection Regulation (GDPR). The GDPR has strict rules on how personal data may be used, and threatens large fines to organizations that do not follow the rules. However, GDPR says that if data is anonymous, then it is not considered personal and does not fall under the rules. Unfortunately, there are no precise guidelines on how to determine if data is anonymous or not. Member states are expected to come up with certification programs for anonymity, but do not know how to do so.

This is where we come in. Paul Francis' group, in research partnership with the startup Aircloak, has been developing an anonymizing technology called Diffix over the last five years. Diffix is an empirical, not a formal technology, and so the question remains "how anonymous is Diffix?" While it may not be possible to precisely answer that question, one way we try to answer that question is through a bounty program: we pay attackers who can demonstrate how to compromise anonymity in our system. Last year we ran the first (and still only) bounty program for anonymity. The program was successful in that some attacks were found, and in the process of designing defensive measures, Diffix has improved.

In order to run the bounty program, we naturally needed a measure of anonymity so that we could decide how much to pay attackers. We designed a measure based on how much confidence an attacker has in a prediction of an individual's data values, among other things. At some point, we realized that our measure applies not just to attacks on Diffix, but to any anonymization system. We also realized that our measure might be useful in the design of certification programs for anonymity.

We decided to develop a general score for anonymity, and to build tools that would allow anyone to apply the measure to any anonymization technology. The score is called the GDA Score, for General Data Anonymity Score.

The primary strength of the GDA Score is that it can be applied to any anonymization method, and therefore is apples-to-apples. The primary weakness is that it is based on empirical attacks (real attacks against real systems), and therefore the score is only as good as the attacks themselves. If there are unknown attacks on a system, then the score won't reflect this and may therefore make a system look more anonymous than it is.

Our hope is that over time enough attacks will be developed that we can have high confidence in the GDA Score. Towards that end, we've started the Open GDA Score Project. This is a community effort to provide software and databases in support of developing new attacks, and a repository where the scores can be viewed. We recently launched the project in the form of a website, www.gda-score.org, and some initial tools and simple attacks. We will continue to develop tools and new attacks, but our goal is to attract broad participation from the community.

For more information, visit www.gda-score.org.

We have launched the Open GDA Score Project at www.gda-score.org.  This is an open project to develop a set of tools and databases to generate anonymity scores for any data anonymization technique. The GDA Score, which stands for General Data Anonymity Score, is the first data anonymization measurement methodology that works with any anonymization technique. The GDA Score is a generalization of the measurement technique developed by Francis' group for the Diffix bounty program run last year. This was the first bounty program for anonymity. The GDA Score is of particular interest in Europe, where member states are expected to produce certification programs for anonymity.

MPI-SWS faculty member Krishna Gummadi and MPI-SWS alumnus Alan Mislove have been awarded a "Secure the Internet" grant by Facebook. Their proposal, “Towards privacy-protecting aggregate statistics in PII-based targeted advertising,” has been awarded $60,000 to develop techniques for revealing advertising statistics that provide hard guarantees of user privacy, based on a (principles-first) approach. Their goal is to develop a differential privacy-like approach that can be applied to existing advertising systems.

The Facebook "Secure the Internet" grant program is designed to improve the security, privacy, and safety of internet users. Gummadi and Mislove's proposal was one of only 10 winning proposals, which were together awarded more than $800,000 by Facebook.

MPI-SWS faculty member Jonathan Mace has received an honorable mention for the Dennis M. Ritchie Doctoral Dissertation Award.

Launched in 2013, the Dennis M. Ritchie Doctoral Dissertation Award was created by the Association for Computing Machinery's Special Interest Group on Operating Systems (ACM SIGOPS) to recognize research in software systems and to encourage the creativity that Dennis Ritchie embodied. Only one winner is chosen annually, and this year, Jonathan Mace's dissertation received an Honorable Mention for the award.

"Many tools for monitoring and enforcing distributed systems," Jonathan explains, "capture information about end-to-end executions by propagating in-band contexts." In his thesis---A Universal Architecture for Cross-Cutting Tools in Distributed Systems---he characterizes a broad class of such cross-cutting tools and extends these ideas to new applications in resource management and dynamic monitoring. Finally, he identifies underlying commonalities in this class of tools, and proposes an abstraction layering that simplifies their development, deployment, and reuse.

The French Data Protection Authority CNIL has recognized the benefits of Diffix anonymization by republishing an article by Paul Francis in which the utility of Diffix anonymization is highlighted. Diffix is the anonymization technology developed in joint research between Francis' group and Aircloak GmbH.

Last year, CNIL published an article titled "Can anonymized data still be useful." The purpose of the article was to demonstrate that strong anonymization does not necessarily prevent useful analytics. In this work, CNIL uses K-anonymity on the New York City taxi database. Inspired by this effort, Francis shows that Diffix can be used for a wide range of analysis on the NYC taxi database, including trip times to LaGuardia airport, taxi driver work profiles, and congestion in the Manhattan financial district.

CNIL re-published the article under the title "Anonymity vs. Utility: Another shot at Anonymizing the New York City taxi dataset".

Jonathan Mace has joined the institute as a tenure-track faculty member, effective September 1, 2018.  He is joining us from Brown University, USA, where he has completed his Ph.D. in computer science.  Jonathan's research focuses on tools, techniques, and abstractions to make it easier to develop and operate cloud distributed systems.  In particular, he is interested in making it easier to reason about and control complicated, end-to-end system behaviors at runtime.

Before starting his Ph.D., Jonathan worked for two years at IBM UK, and earned his undergraduate degree from Oxford University.  He is a recipient of the Facebook Fellowship in Distributed Systems, an SOSP Best Paper Award, and the Honorable Mention for the Dennis Ritchie Thesis Award.

Antoine Kaufmann is joining us from the University of Washington in Seattle,
where he obtained his Ph.D. in computer science. His research investigates the
design and implementation of efficient, scalable, and robust systems for
rapidly evolving modern platforms, with a current focus on data centers. He
addresses these challenges from a systems perspective, with solutions that span
multiple layers of the systems stack, from operating systems through networks
down to hardware, but also programming languages and applications.

Antoine joins the institute as a research group leader, effective Aug 6, 2018. Before
his Ph.D., Antoine obtained his Master's and Bachelor's degree from ETH Zurich.

Bug bounty programs are a popular way to find security flaws in deployed systems. We are the first to use a bounty program to find flaws in anonymization schemes, namely the anonymization scheme we designed called Diffix. We take an empirical approach to anonymization rather than the more common formal approach. The empirical approach leads to anonymization schemes with high utility, but also uncertainties about the anonymization properties. The bounty program helps build understanding and confidence in Diffix. To learn more, visit challenge.aircloak.com

Applications are invited for faculty positions at all career stages in computer science, with a particular emphasis on systems (broadly construed). We expect multiple positions to be filled in systems, but exceptional candidates in other areas of computer science are also strongly encouraged to apply.

A doctoral degree in computer science or related areas and an outstanding research record (commensurate for the applicant's career stage) are required. Successful candidates are expected to build a team and pursue a highly visible research agenda, both independently and in collaboration with other groups.

MPI-SWS is part of a network of over 80 Max Planck Institutes, Germany's premier basic-research organisations. MPIs have an established record of world-class, foundational research in the sciences, technology, and the humanities. The institute offers a unique environment that combines the best aspects of a university department and a research laboratory: Faculty enjoy full academic freedom, lead a team of doctoral students and post-docs, and have the opportunity to teach university courses; at the same time, they enjoy ongoing institutional funding in addition to third-party funds, a technical infrastructure unrivaled for an academic institution, as well as internationally competitive compensation.

The institute is located in the German cities of Saarbruecken and Kaiserslautern, in the tri-border area of Germany, France, and Luxembourg. We maintain an international and diverse work environment and seek applications from outstanding researchers worldwide. The working language is English; knowledge of the German language is not required for a successful career at the institute.

Qualified candidates should apply on our application website (apply.mpi-sws.org). To receive full consideration, applications should be received by December 1st, 2017.

The institute is committed to increasing the representation of minorities, women, and individuals with physical disabilities. We particularly encourage such individuals to apply. The initial tenure-track appointment is for five years; it can be extended to seven years based on a midterm evaluation in the fourth year. A permanent contract can be awarded upon a successful tenure evaluation in the sixth year.

MPI-SWS researchers—faculty members Krishna Gummadi and Peter Druschel and former SWS doctoral students Alan Mislove and Massimiliano Marcon—have received the ACM SIGCOMM Test of Time Award for their IMC 2007 paper on "Measurement and Analysis of Online Social Networks." The work was done in collaboration with Bobby Bhattacharjee of the University of Maryland.

The award citation reads as follows: "This is one of the first papers that examine multiple online social networks at scale. By introducing novel measurement techniques, the paper has had an enduring influence on the analysis, modeling and design of modern social media and social networking services."

The session, entitled “Challenges and Strategies for Certifying Data Anonymization for Data Sharing,” brings together technical and legal experts to explore how Data Protection Officers (DPOs) can manage the complexities and uncertainties of GDPR-compliant data anonymization. The IAPP Congress will be held November 7-9 in Brussels.

Session Abstract:

Data sharing is increasingly important. Companies share data internally across business units to gain business insights, they share data externally with data analytics vendors, and they often share data simply to make money. Ensuring the anonymity of users in the data set is necessary. The process of approving or certifying anonymization however is costly, time consuming, and uncertain. Current approaches to anonymization are ad hoc at best. They require a custom strategy for each new data sharing scenario, and it is often unclear whether the data is really anonymized or not.

In this informative and lively session, corporate DPOs, vendors of analytics solutions, and privacy researchers share their experiences with data anonymization and the approval process. They provide case studies illustrating the pitfalls of "do it yourself" anonymization, and show how some new ready-for-use anonymization can eliminate the delays and guesswork of data anonymization.

Paul Francis will give the keynote address at the Oakland (IEEE S&P) Workshop on Privacy Engineering. The talk, entitled "The Diffix Framework: Revisiting Noise, Again", presents the first database anonymization system that exhibits low noise, unlimited queries, simple configuration, and rich query semantics while still giving strong anonymity.

The workshop will be held May 25 in San Jose, CA.

Talk Abstract:

For over 40 years, the holy grail of database anonymization is a system that allows a wide variety of statistical queries with minimal answer distortion, places no limits on the number of queries, is easy to configure, and gives strong protection of individual user data.  This keynote presents Diffix, a database anonymization system that promises to finally bring us within reach of that goal.  Diffix adds noise to query responses, but "fixes" the noise to the response so that repeated instances of the same response produce the same noise.  While this addresses the problem of averaging attacks, it opens the system to "difference attacks" which can reveal individual user data merely through the fact that two responses differ.  Diffix proactively examines queries and responses to defend against difference attacks.  This talk presents the design of Diffix, gives a demo of a commercial-quality implementation, and discusses shortcomings and next steps.

The paper "A Broad View of the Ecosystem of Socially Engineered Exploit Documents" was accepted at NDSS '17 (Network and Distributed System Security Symposium).  The authors include Stevens Le Blond, Cédric Gilbert, Utkarsh Upadhyay, and Manuel Gomez Rodriguez from MPI-SWS, as well as David Choffnes from Northeastern University.

Our understanding of exploit documents as a vector to deliver targeted malware is limited to a handful of studies done in collaboration with the Tibetans, Uyghurs, and political dissidents in the Middle East. In this measurement study, we present a complementary methodology relying only on publicly available data to capture and analyze targeted attacks with both greater scale and depth. In particular, we detect exploit documents uploaded over one year to a large anti-virus aggregator (VirusTotal) and then mine the social engineering information they embed to infer their likely targets and contextual information of the attacks. We identify attacks against two ethnic groups (Tibet and Uyghur) as well as 12 countries spanning America, Asia, and Europe. We then analyze the exploit documents dynamically in sandboxes to correlate and compare the exploited vulnerabilities and malware families targeting different groups. Finally, we use machine learning to infer the role of the uploaders of these documents to VirusTotal (i.e., attacker, targeted victim, or third-party), which enables their classification based only on their metadata, without any dynamic analysis. We make our datasets available to the academic community.

MPI-SWS Ph.D. student Aastha Mehta was selected to attend the 4th annual Heidelberg Laureate Forum in September 2016. An international committee of experts selected Aastha for one of only 200 spots reserved for young computer scientists and mathematicians from around the world. In addition to participating in the forum, she was one of 6 researchers invited for a blog interview. Aastha was provided funding to attend the forum through a Romberg Grant.

The Heidelberg Laureate Forum gives young computer science and math researchers the opportunity to interact with some of the world's top scientists. The twenty speakers for the 2016 Forum, for example, include 12 different Turing Award winners, as well as numerous winners of the Fields Medal and the Abel Prize.

MPI-SWS spinoff Aircloak has won the 2014 Cisco Internet of Things (IoT) Security Grand Challenge. Aircloak was selected for its innovative approach to privacy protection—it is building the world's first anonymized analytics system. As a grand challenge award winner, Aircloak was awarded a $75,000 cash prize and was showcased at the IoT World Forum. In addition, the award also provides the Aircloak team with mentoring, training and access to business expertise from Cisco and other supporting organizations, as well as potential investment and partnering opportunities in the future. For more info see the Cisco award announcement (in English or in German), and the Cisco blog.

MPI-SWS directors Peter Druschel and Rupak Majumdar, along with Gerhard Weikum (Scientific Director at the MPI for Informatics) and Michael Backes (MPI-SWS Fellow and Professor at Saarland University), have jointly been awarded the prestigious ERC Synergy Grant.

Over the next six years their project "imPACT: Privacy, Accountability, Compliance, and Trust in Tomorrow's Internet" will receive almost 10 million euros, which will allow them to explore how to protect users against eavesdropping and fraud on the Internet without restricting trade, freedom of expression or access to information.

Lorenzo Alvisi, a professor at the University of Texas at Austin, has been selected for a prestigious Humboldt Research Award from the Alexander von Humboldt Foundation. This award provides support for him to spend up to a year at the institute, where he will work with Peter Druschel and other MPI-SWS researchers on fault-tolerant computing for multi-core servers.

This is the second year that an MPI-SWS visiting professor has received a Humboldt Award. Johannes Gehrke was a 2010 Humboldt Research Award recipient.

The Humboldt Research Award is granted "in recognition of a researcher's entire achievements to date to academics whose fundamental discoveries, new theories, or insights have had a significant impact on their own discipline and who are expected to continue producing cutting-edge achievements in the future."

We are pleased to announce that two new faculty will join MPI-SWS.



Allen Clement obtained his Ph.D. at the University of Texas at Austin in 2011. Allen's research aims at designing and building systems that continue to work despite the myriad of things that go 'wrong' in deployed systems, including broken components, malicious adversaries, and benign race conditions. His research builds on techniques from distributed systems, security, fault tolerance, and game theory.



Cristian Danescu-Niculescu-Mizil is joining us from Cornell University, where he obtained his PhD in computer science. Cristian's research aims at developing computational frameworks that can lead to a better understanding of human social behavior, by unlocking the unprecedented potential of the large amounts of natural language data generated online. His work tackles problems related to conversational behavior, opinion mining, computational semantics and computational advertising.

MPI-SWS faculty member Paul Francis has received the ACM SIGCOMM Test of Time Award for 2011.

The ACM SIGCOMM Test of Time Award recognizes papers published 10 to 12 years in the past in Computer Communication Review or any SIGCOMM sponsored or co-sponsored conference that is deemed to be an outstanding paper whose contents are still a vibrant and useful contribution today.

Paul's 2001 paper, "A Scalable Content-Addressable Network," was coauthored with Mark Handley, Richard Karp, Sylvia Ratnasamy, and Scott Shenker. This paper is one of four highly influential papers that laid the foundation for P2P systems based on distributed hash tables (DHTs).

We are pleased to announce that three new faculty will join MPI-SWS this fall.

Björn Brandenburg is joining us from the University of North Carolina at Chapel Hill (UNC), where he obtained his Ph.D. in computer science. Björn's research interests include multiprocessor real-time system, real-time synchronization protocols, and operating systems. Björn is the lead designer and developer of LITMUS^RT, an extension of the Linux kernel for real-time scheduling and synchronization on multicore platforms.

Deepak Garg is joining us from the Cybersecurity Lab (CyLab) at Carnegie Mellon University, where he was a post-doctoral researcher. He obtained his Ph.D. from Carnegie Mellon's Computer Science Department. His research interests are in the areas of computer security and privacy, formal logic and programming languages. He is specifically interested in logic-based models of secure systems and formal analysis of security properties of systems.

Ruzica Piskac is joining us from EPFL, where she has completed her Ph.D. in computer science. The goal of her research is to make software development easier and software more reliable via automated reasoning techniques. She is specifically interested in decision procedures, their combinations and applications in program verification and software synthesis.

Two MPI-SWS alumni — Andreas Haeberlen and Alan Mislove — have received NSF CAREER awards. The CAREER award is the National Science Foundation's most prestigious award in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research.

Andreas Haeberlen, now an Assistant Professor in the department of Computer and Information Science at the University of Pennsylvania, has received the award for his proposal "Evidence in Federated Distributed Systems."

Alan Mislove, now an Assistant Professor in the College of Computer and Information Science at Northeastern University, has received the award for his proposal "Systems for the Emerging Patterns of Content Exchange."

Johannes Gehrke, a professor at Cornell University, has been selected for a prestigious Humboldt Research Award from the Alexander von Humboldt Foundation. This award will provide support for him to spend eight months in Germany, working with Peter Druschel and other MPI-SWS researchers on data-intensive distributed systems that make up the software infrastructure inside such large Web companies as Amazon, Yahoo! and Google.

The Humboldt Research Award is granted "in recognition of a researcher's entire achievements to date to academics whose fundamental discoveries, new theories, or insights have had a significant impact on their own discipline and who are expected to continue producing cutting-edge achievements in the future."

Andreas Haeberlen has been awarded the 2009 Otto Hahn Medal for outstanding scientific achievement. The medal, and its accompanying monetary prize, will be presented to Andreas at the Max Planck society's annual General Assembly in Hannover on June 16. Andreas's medal was awarded for "pioneering work on accountability in distributed computer systems, in particular for the design, implementation and demonstration of practical techniques for the reliable and tamper-proof detection of complex faults." Andreas obtained his PhD in Spring 2009 and is now an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania.

Founded in 1948, the Max Planck Society is a non-profit scientific organization affiliated with the Max Planck Institutes. The Society awards the Otto Hahn Medal annually to young scientists in recognition of outstanding scientific achievement. In addition to a stipend, the award gives winners preference for grants enabling them to conduct research abroad for one year.

MPI-SWS fellow Michael Backes has been honored as a recipient of the ERC Starting Grant 2009. Michael was also recently selected by the editors of Technology Review as one of the 35 young innovators under the age of 35 whose work they found most exciting.

The ERC Starting Grant was established in 2007 by the European Research Council to support up-and-coming research leaders in Europe. Recipients are selected based upon "outstanding track-record of early achievements appropriate to their research field and career stage."

Michael's 2009 Young Innovator award is based on his work proving that Internet security protocols can really be trusted. Software designed by Backes' group can prove in less than a second whether an Internet protocol is truly secure.

Michael received his Ph.D. from Saarland University in 2002. He was a Research Staff Member at the IBM Zurich Research laboratory before accepting his current position as a professor at Saarland University in 2006. He was named a fellow of the Max-Planck Institute for Software Systems in 2007.

MPI-SWS faculty Peter Druschel has been honored as the eighth recipient of the Mark Weiser Award -- the top international award in the field of operating systems.

The Mark Weiser Award was established in 2001 by ACM's Special Interest Group on Operating Systems. Recipients must have begun their careers no earlier than 20 years prior to nomination, and they are selected based upon "contributions that are highly creative, innovative, and possibly high-risk, in keeping with the visionary spirit of Mark Weiser."

In the award ceremony, Peter's broad and high-impact contributions in his research field were highlighted, including work such as the Pastry peer-to-peer system, the Flash web server, the Fbufs operating system support for high-speed networking, and his work on resource management in large-scale servers.

Peter received his Ph.D. from the University of Arizona in 1994. He was a Professor of Computer Science at Rice University, before accepting his current position as the founding director of the Max Planck Institute for Software Systems.

Previous recipients of the Mark Weiser Award are Frans Kaashoek (MIT), Mendel Rosenblum (Stanford), Mike Burrows (Google), Brian Bershad (Univ. of Washington), Tom Anderson (Univ. of Washington), Dawson Engler (Stanford), and Peter Chen (Univ. of Michigan)

Krishna Gummadi, Ph.D., accepts a position on the faculty of the MPI for Software Systems as an independent researcher. This position is comparable to a tenure-track Assitant Professor position at a U.S. University.

Krishna hold a B.Tech. degree from IIT Madras, and the M.S. and Ph.D. degrees from the University of Washington in Seattle, all in Computer Science and Engineering. Krishna has gained international recognition for his research on networked systems, as the recipient of three best paper awards at leading conferences in his area and as the main author of the most cited computer science articles in 2003 and 2004, respectively, according to citeseer. He will join the MPI for Software Systems in October 2005.

Prof. Peter Druschel, Ph.D., accepts the position of Founding Director of the MPI for Software Systems. Peter comes from Rice University in Houston, TX, where he has spent eleven years as Assistant Professor (1994-2000), Associate Professor (2000-2002) and Full Professor (2002-) of Computer Science. Peter also spent time teaching and researching at the University of Paris VI, at Microsoft Research in Cambridge, UK, and at the Massachussets Institute of Technology (MIT) in Boston. He holds a Dipl.-Ing. (FH) degree in Electrical Engineering from Fachhochschule Munich, and the M.S. and Ph.D. degrees in Computer Science from the University of Arizona. Peter conducts research in experimental distributed systems, with a focus on self-organizing, decentralized and autonomous systems. He is the recipient of a National Science Foundation CAREER Award and an Alfred P. Sloan Fellowship.

In the spring of 2009, MPI-SWS graduated its first four PhD students—Andreas Haeberlen, Alan Mislove, Animesh Nandi, and Atul Singh. All four students have landed competitive academic or research positions in a very tight job market.

This fall, Andreas Haeberlen will be an Assistant Professor at the University of Pennsylvania, Alan Mislove will be an Assistant Professor at Northeastern University, Animesh Nandi will be a researcher at Bell Labs, India, and Atul Singh will be a researcher at NEC Labs, Princeton. The students received their PhD degrees from Rice University after spending the last several years of their graduate studies at MPI-SWS.

Paul Francis joins the institute's faculty as a scientific director. Paul's work over the years has focused on network routing and addressing problems, with a particular interest in large and self-configuring systems.

Paul's work has had tremendous impact on both research and industrial practice. He is best known for inventing Network Address Translation (NAT), shared multicast trees (which form the basis of PIM-SM), and the use of multiple addresses to scale routing in the face of site multihoming, which was adopted by IPv6.

Paul joins MPI-SWS from Cornell University, where he was on the faculty of the computer science department. Prior to that, Paul spent many years in industry labs such as Bellcore, NTT Research Labs in Tokyo, ACIRI in Berkeley, and at several Silicon Valley startups.

Paul Francis's arrival marks the opening of the insitute's Kaiserslautern site.

We are pleased to announce that three new faculty will join MPI-SWS.

Rodrigo Rodrigues will lead a research group on Dependable Systems. He obtained his Ph.D. from the Massachusetts Institute of Technology and joins us from the Instituto Superior Tecnico in Lisbon.

Derek Dreyer will lead a research group on Type Systems and Functional Programming. He obtained his Ph.D. from Carnegie Mellon University and joins us from the Toyota Technological Institute at Chicago.

Andrey Rybalchenko will lead a research group on Verification Systems. He previously held a post-doctoral position with Tom Henzinger at EPFL. He is the winner of the Otto-Hahn-Medal of the Max Planck Society.