Tag Archives: privacy

CFP: PPML Workshop at NIPS 2018

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Privacy Preserving Machine Learning

NIPS 2018 Workshop

Montreal, December 8, 2018

Description

This one day workshop focuses on privacy preserving techniques for training, inference, and disclosure in large scale data analysis, both in the distributed and centralized settings. We have observed increasing interest of the ML community in leveraging cryptographic techniques such as Multi-Party Computation (MPC) and Homomorphic Encryption (HE) for privacy preserving training and inference, as well as Differential Privacy (DP) for disclosure. Simultaneously, the systems security and cryptography community has proposed various secure frameworks for ML. We encourage both theory and application-oriented submissions exploring a range of approaches, including:

  • secure multi-party computation techniques for ML
  • homomorphic encryption techniques for ML
  • hardware-based approaches to privacy preserving ML
  • centralized and decentralized protocols for learning on encrypted data
  • differential privacy: theory, applications, and implementations
  • statistical notions of privacy including relaxations of differential privacy
  • empirical and theoretical comparisons between different notions of privacy
  • trade-offs between privacy and utility

We think it will be very valuable to have a forum to unify different perspectives and start a discussion about the relative merits of each approach. The workshop will also serve as a venue for networking people from different communities interested in this problem, and hopefully foster fruitful long-term collaboration.

Submission Instructions

Submissions in the form of extended abstracts must be at most 4 pages long (not including references) and adhere to the NIPS format. We do accept submissions of work recently published or currently under review. Submissions should be anonymized. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have a link to arxiv or a pdf published on the workshop webpage.

Program Committee

  • Pauline Anthonysamy (Google)
  • Borja de Balle Pigem (Amazon)
  • Keith Bonawitz (Google)
  • Emiliano de Cristofaro (University College London)
  • David Evans (University of Virginia)
  • Irene Giacomelli (Wisconsin University)
  • Nadin Kokciyan (King’s College London)
  • Kim Laine (Microsoft Research)
  • Payman Mohassel (Visa Research)
  • Catuscia Palamidessi (Ecole Polytechnique & INRIA)
  • Mijung Park (Max Planck Institute for Intelligent Systems)
  • Benjamin Rubinstein (University of Melbourne)
  • Anand Sarwate (Rutgers University)
  • Philipp Schoppmann (HU Berlin)
  • Nigel Smart (KU Leuven)
  • Carmela Troncoso (EPFL)
  • Pinar Yolum (Utrecht University)
  • Samee Zahur (University of Virginia)

Organizers

  • Adria Gascon (Alan Turing Institute & Edinburgh)
  • Niki Kilbertus (MPI for Intelligent Systems & Cambridge)
  • Olya Ohrimenko (Microsoft Research)
  • Mariana Raykova (Yale)
  • Adrian Weller (Alan Turing Institute & Cambridge)
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Hello from the IPAM Workshop on Privacy for Biomedical Data

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I just arrived in LA for the IPAM Workshop on Algorithmic Challenges in Protecting Privacy for Biomedical Data. I co-organized this workshop with Cynthia Dwork, James Zou, and Sriram Sankararaman and it is (conveniently) before the semester starts and (inconveniently) overlapping with the MIT Mystery Hunt. The workshop has a really diverse set of speakers so to get everyone on the same page and anchor the discussion, we have 5 tutorial speakers and a few sessions or shorter talks. The hope is that these tutorials (which are on the first two days of the workshop) will give people some “common language” to discuss research problems.

The other big change we made to the standard workshop schedule was to put in time for “breakout groups” to have smaller discussions focused on identifying the key fundamental problems that need to be addressed when thinking about privacy and biomedical data. Because of the diversity of viewpoints among participants, it seems a tall order to generate new research collaborations out of attending talks and going to lunch. But if we can, as a group, identify what the mathematical problems are (and maybe even why they are hard), this can help identify the areas of common interest.

I think of these as falling into a few different categories.

  • Questions about demarcation. Can we formalize (mathematically) the privacy objective in different types of data sets/computations? Can we use these to categorize different types of problems?
  • Metrics. How do we formulate the privacy-utility tradeoffs for different problems? What is the right measure of performance? What (if anything) do we lose in guaranteeing privacy?
  • Possibility/impossibility. Algorithms which can guarantee privacy and utility are great, but on the flip side we should try to identify when privacy might be impossible to guarantee. This would have implications for higher-level questions about system architectures and policy.
  • Domain-specific questions. In some cases all of the setup is established: we want to compute function F on dataset D under differential privacy and the question is to find algorithms with optimal utility for fixed privacy loss or vice versa. Still, identifying those questions and writing them down would be a great outcome.

In addition to all of this, there is a student poster session, a welcome reception, and lunches. It’s going to be a packed 3 days, and although I will miss the very end of it, I am excited to learn a lot from the participants.

IPAM Workshop on Algorithmic Challenges in Protecting Privacy for Biomedical Data

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IPAM is hosting a workshop on “Algorithmic Challenges in Protecting Privacy for Biomedical Data” which will be held at IPAM from January 10-12, 2018.

The workshop will be attended by many junior as well as senior researchers with diverse backgrounds. We want to to encourage students or postdoctoral scholars who might be interested, to apply and/or register for this workshop.

I think it will be quite interesting and has the potential to spark a lot of interesting conversations around what we can and cannot do about privacy for medical data in general and genomic data in specific.

Postdoc at ASU/Harvard on ML and Privacy

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A joint postdoc position at Arizona State University and Harvard University in the area of machine learning and privacy is available immediately. The successful candidate will be working with the research groups of Prof. Lalitha Sankar and Prof. Flavio du Pin Calmon.

Specific topics of focus are the interplay of machine learning and privacy with focus on both rigorous information-theoretical results as well as practical design aspects.

The appointment will be for a period of 12 months initially, with a possibility for renewal. We are looking for strong applicants with an excellent theoretical background and a proven capacity for high-quality research in form of a strong publication record. Knowledge of privacy literature is desirable.

Interested applicants should submit a current CV, a 1-page research statement and a list of three references. Candidates should contact us via email at lsankar@asu.edu and/or flavio@seas.harvard.edu.

CFP: T-SIPN Special Issue on Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems

GUEST EDITORS:

SCOPE
The focus of this special issue is on distributed information acquisition, estimation, and adaptive learning for security and privacy in the context of networked cyber-physical systems (CPSs) which are engineering systems with integrated computational and communication capabilities that interact with humans through cyber space. The CPSs have recently emerged in several practical applications of engineering importance including aerospace, industrial/manufacturing process control, multimedia networks, transportation systems, power grids, and medical systems. The CPSs typically consist of both wireless and wired sensor/agent networks with different capacity/reliability levels where the emphasis is on real-time operations, and performing distributed, secure, and optimal sensing/processing is the key concern. To satisfy these requirements of the CPSs, it is of paramount importance to design innovative “Signal Processing” tools to provide unprecedented performance and resource utilization efficiency.

A significant challenge for implementation of signal processing solutions in CPSs is the difficulty of acquiring data from geographically distributed observation nodes and storing/processing the aggregated data at the fusion center (FC). As such, there has been a recent surge of interest in development of distributed and collaborative signal processing technologies where adaptation, estimation, and/or control are performed locally and communication is limited to local neighborhoods. Distributed signal processing over networked CPSs, however, raise significant privacy and security concerns as local observations are being shared by neighboring nodes in a collaborative and iterative fashion. On one hand, applications of CPSs are severely safety critical where potential cyber and physical attacks by adversaries on signal processing modules could lead to a variety of severe consequences including customer information leakage, destruction of infrastructures, and endangering human lives. On the other hand, the need for cooperation be- tween neighboring nodes makes it imperative to prevent the disclosure of sensitive local information during distributed information fusion step. At the same time, efficient usage of available resources (communication, computation, bandwidth, and energy) is a pre-requisite for productive operation of the CPSs. To accommodate these critical aspects of CPSs, it is of great practical importance and theoretical significance to develop advanced “Secure and Privacy Preserving Distributed Signal Processing” solutions.

The spirit and wide scope of distributed signal processing in revolutionized CPSs calls for novel and innovative techniques beyond conventional approaches to provide precise guarantees on security and privacy of CPSs. The objective of this special issue is to further advance recent developments of distributed signal processing to practical aspects of CPSs for real-time processing and monitoring of the underlying system in a secure and privacy preserving manner while avoiding degradation of the processing performance and preserving the valuable resources. To provide a systematic base for future advancements of CPSs, this special issue aims to provide a research venue to investigate distributed signal processing techniques with adaptation, cooperation, and learning capabilities which are secure against cyber-attacks and protected against privacy leaks. The emphasis of this special issue is on distributed/network aspects of security and privacy in CPSs. Papers with primary emphasis on forensics and security will be redirected to IEEE Transactions on Information Forensics and Security (TIFS). Topics of interest include, but are not limited to:

  • Security and Privacy of distributed signal processing in networked CPSs.
  • Distributed and secure detection, estimation, and information fusion.
  • Security and privacy of consensus and diffusive strategies in networked systems.
  • Secure and privacy preserving distributed adaptation and learning.
  • Security and privacy of distributed sensor resource management in networked systems.
  • Distributed event-based estimation/control in networked CPSs.
  • Detection and identification of potential attacks on distributed signal processing mechanisms.
  • Application domains including but not limited to, smart grids, camera networks, multimedia network, and vehicular networks.

SUBMISSION GUIDELINES
Authors are invited to submit original research contributions by following the detailed instructions given in the “Information for Authors” page or TSIPN page. Manuscripts should be submitted via Scholar One(Manuscript Central) system. Questions about the special issue should be directed to the Guest Editors.

IMPORTANT DATES:

    • Paper submission deadline: December 15, 2016
    • Notification of the first review: March 1, 2017
    • Revised paper submission: April 15, 2017
    • Notification of the re-review: June 15, 2017
    • Minor revision deadline: August 1, 2017
    • Final notification: September 1, 2017
    • Final manuscript due: October 15, 2017

Publication: Advance posting in IEEExplore as soon as authors approve galley proofs

Expected inclusion in an issue: March 2018

Signal boost: DPCOMP.ORG is live

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I got the following email from Gerome Miklau:

Dear colleagues:

We are writing to inform you of the launch of DPCOMP.ORG.

DPCOMP.ORG is a public website designed with the following goals in mind: (1) to increase the visibility and transparency of state-of-the-art differentially private algorithms and (2) to present a principled and comprehensive empirical evaluation of these algorithms. The intended audience is both researchers who study privacy algorithms and practitioners who might deploy these algorithms.

Currently DPComp includes algorithms for answering 1- and 2-dimensional range queries. We thoroughly study algorithm accuracy and the factors that influence it and present our findings using interactive visualizations. We follow the evaluation methodology from the paper “Principled Evaluation of Differentially Private Algorithms using DPBench”. In the future we plan to extend it to cover other analysis tasks (e.g., higher dimensional data, private regression).

Our hope is that the research community will contribute to improving DPCOMP.ORG so that practitioners are exposed to emerging research developments. For example: if you have datasets which you believe would distinguish the performance of tested algorithms, new algorithms that could be included, alternative workloads, or even a new error metric, please let us know — we would like to include them.

Please share this email with interested colleagues and students. And we welcome any feedback on the website or findings.

Sincerely,

Michael Hay (Colgate University)
Ashwin Machanavajjhala (Duke University)
Gerome Miklau (UMass Amherst)

IHP “Nexus” Workshop on Privacy and Security: Day 2

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Verrrrrry belated blogging on the rest of the workshop, more than a month later. Day 2 had 5 talks instead of the tutorial plus talks, and the topics were a bit more varied (this was partly because of scheduling issues that prevented us from being strictly thematic).

Amos Beimel started out with a talk on secret sharing, which had a very nice tutorial/introduction to the problem, including the connection between Reed-Solomon codes and Shamir’s t-out-of-n scheme. For professional (and perhaps personal) reasons I found myself wondering how much more the connection between secret sharing and coding theory was — after all, this was a workshop about communication between information theory and theoretical CS. Not being a coding theory expert myself, I could only speculate. What I didn’t know about was the more general secret sharing structures and the results of Ito-Saito-Nishizeki scheme (published in Globecom!). Amos also talked about monotone span programs, which were new to me, and how to prove lower bounds. He concluded with more recent work on the related distribution design problem: how can we construct a distribution on n variables given constraints that specify subsets which should have identical marginals and subsets which should have disjoint support? The results appeared in ICTS.

Ye Wang talked about his work on common information and how it appears in privacy and security problems from an information theoretic perspective. In particular he talked about secure sampling, multiparty computation, and data release problems. The MPC and sampling results were pretty technical in terms of notions of completeness of primitives (conditional distributions) and triviality (a way of categorizing sources). For the data release problem he focused on problems where a sanitizer has access to a pair (X,Y) where X is private and Y is “useful” — the goal is to produce a version of the data which reveals less about X (privacy) and more about Y (utility). Since they are correlated, there is a tension. The question he addressed is when having access to Y alone as as good as both X and Y.

Manoj, after giving his part of the tutorial (and covering for Vinod), gave his own talk on what he called “cryptographic complexity,” which is an analogy to computational complexity, but for multiparty functions. This was also a talk about definitions and reductions: if you can build a protocol for securely computing f(\cdot) using a protocol for g(\cdot), then f(\cdot) reduces to g(\cdot). A complete function is one for which everything reduces to it, and a trivial function reduces to everything. So with the concepts you can start to classify and partition out functions like characterizing all complete functions for 2 parties, or finding trivial functions under different security notions. He presented some weird facts, like an n bit XOR doesn’t reduce to an (n-1) bit XOR. It was a pretty interesting talk, and I learned quite a bit!

Elette Boyle gave a great talk on Oblivious RAM, a topic about which I was completely oblivious myself. The basic idea in oblivious RAM is (as I understood it) that an adversary can observe the accesses to a RAM and therefore infer what program is being executed (and the input). To obfuscate that, you introduce a bunch of spurious accesses. So if you have a program $\latex \Pi$ whose access pattern is fixed prior to execution, you can randomize the accesses and gain some security. The overhead is the ratio of the total accesses to the required accesses. After this introduction to the problem, she talked about lower bounds on the overhead (e.g. you need this much overhead) for a case where you have parallel processing. I admit that I didn’t quite understand the arguments, but the problem was pretty interesting.

Hoeteck Wee gave the last (but quite energetic) talk of the afternoon, on what he called “functional encryption.” The ideas is that Alice has (x,M) and Bob has y. They both send messages to a third party, Charlie. There is a 0-1 function (predicate) P(x,y) such that if P(x,y) = 1 then Charlie can decode the message M. Otherwise, they cannot. An example would be the predicate P(x,y) = \mathbf{1}(x = y). In this case, Alice can send h(x) \oplus M and Bob can send h(y) for some 2-wise independent hash function, and then Charlie can recover M if the hashes match. I think there is a question in this scheme about whether Charlie needs to know that they got the right message, but I guess I can read the paper for that. The kinds of questions they want to ask are what kinds of predicates have nice encoding schemes? What is the size of message that Alice and Bob have to send? He made a connection/reduction to a communication complexity problem to get a bound on the message sizes in terms of the communication complexity of computing the predicate P. It really was a very nice talk and pretty understandable even with my own limited background.

Call for Papers: T-SIPN Special Issue on Distributed Information Processing in Social Networks

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

  • Dynamic social networks: time varying network topology, edge weights, etc.
  • Social learning, distributed decision-making, estimation, and filtering
  • Consensus and coordination in multi-agent networks
  • Modeling and inference for information diffusion and rumor spreading
  • Multi-layered social networks where social interactions take place at different scales or modalities
  • Resource allocation, optimization, and control in multi-agent networks
  • Modeling and strategic considerations for malicious behavior in networks
  • Social media computing and networking
  • Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
  • Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
  • Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

Manuscript submission due: September 15, 2016
First review completed: November 1, 2016
Revised manuscript due: December 15, 2016
Second review completed: February 1, 2017
Final manuscript due: March 15, 2017
Publication: June 1, 2017

Guest Editors:

Zhenliang Zhang, Qualcomm Corporate R&D (zhenlian@qti.qualcomm.com)
Wee Peng Tay, Nanyang Technological University (wptay@ntu.edu.sg)
Moez Draief, Imperial College London (m.draief@imperial.ac.uk)
Xiaodong Wang, Columbia University (xw2008@columbia.edu)
Edwin K. P. Chong, Colorado State University (edwin.chong@colostate.edu)
Alfred O. Hero III, University of Michigan (hero@eecs.umich.edu)

Mathematical Tools of Information-Theoretic Security Workshop: Days 2-3

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I took sketchier notes as the workshop progressed, partly due to the ICASSP deadline, but also because jet lag started to hit me. The second day was a half day, which started with Zhenjie Zhang giving a tutorial on differential privacy from a databases/data mining perspective and my talk on more machine learning aspects. In between us was a talk by Ben Smyth on building automatic verification for security protocols. Basically you write the protocol as a program and then the ProVerif verifier will go and try to break your protocol. As an example, it can automatically find/generate a man-in-the-middle attack if one exists. I thought it was pretty neat, especially after having recently talked to someone about automatic proof systems. It’s based on something called the applied pi calculus, which I did not understand at all, but hey, I learned something new, which was great. The last two talks of the day were by Lalitha Sankar and Mari Kobayashi. Lalitha talked about mutual information based measures of privacy leakage in an interactive communication setting that is the information-theoretic analogue of communication complexity models in CS. Mari talked about the broadcast channel with state feedback. This is trying to find secure analogues of these opportunistic multicast settings where you need to also generate a secret key.

The last day was on quantum! I learned a lot and took few notes, unfortunately. Andreas Winter gave a tutorial on quantum (the slides for most talks are online and his are as well) and Ciara Morgan discussed the challenges in proving a strong converse for the the capacity of quantum channels. Damian Markham talked about secret sharing in quantum systems. Masahito Hayashi gave a very densely-packed talk surveying a large number of results based on secure randomness extraction and hash functions using Rényi information measures. I think privacy amplification is really interesting but I think I need a tutorial on it before I can really get the research results. The last non-overview talk I have notes on was by David Elkouss (apologies to the remaining speakers): this was a really interesting presentation on how to decide which of two channels is better from a quantum communication sense. The slides are a little engimatic, but the papers are online.

Shlomo Shamai made it to the last day of the workshop (the intersection with High Holidays was unfortunate) — he talked about the layered secrecy view of the broadcast channel: rather than thinking only of the secret message as carrying information, one can think of certain layers (c.f. superposition coding) as being secured based on the channel to the non-legitimate receiver. For example, in a degraded broadcast channel, the strong receiver’s message can sometimes be thought of as secret from the weak receiver. This leads to a raft of models and setups based on who wants to keep what secret from whom, shedding some light on standard superposition, rate splitting, binning, and embedding constructions. The talk was largely based on a paper in the current issues of the Proceedings of the IEEE.

All in all, this was a really great workshop, and the organizers were very generous in the organization.

Postdoc in privacy and security at Imperial College London

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Denis Gündüz is looking for a postdoctoral researcher in the areas of privacy and security in cyber-physical systems, particularly for smart metering applications in smart grids. The position is in the Intelligent Systems and Networks Group within the Electrical and Electronic Engineering Department of Imperial College London.

Previous research experience and a strong track record in information theory, signal processing, and/or optimisation theory is required. This position will be supported through an international project, and will provide an excellent opportunity to work within an interdisciplinary team spanning top European institutions: Imperial College London, KTH, ETHZ and INRIA.
The position is available immediately for one year, with a potential to be extended another year depending on candidate’s performance.

Contact Dr. Gündüz directly if interested.