Speculative reordering for a latency-optimized privacy protection in complex event processing

With increasing number of applications in Internet of Things (IoT), Complex Event Processing (CEP) has already become one of the state-of-the-art technologies recently. In CEP, privacy needs to be considered carefully because events with user’s sensitive information may be exposed to outside world. However, most privacy issues in CEP mainly focus on attribute-based events without considering pattern-based events. There are two important works for pattern-based privacy in CEP: suppression and re-ordering. The former suppresses events belonging to private patterns while the later tends to reorder them. The re-ordering mechanism shows better performance in terms of QoS, but the latency would be long when the size of window increases. Also, the re-ordering strategy is performed only at the end of the windows. In this thesis, we extend the Re-ordering strategy by using speculation based on Markov chains, so we start speculating whether the private pattern occurs in current window before the end of the window. If the private pattern is predicted to occur, we then already re-order events that are part of private patterns. Additionally, the top-k preserving algorithm is introduced for preserving public patterns. Our evaluation results show that we maintain nearly 80 % utility when compared to the normal re-ordering strategy. From our experiments, it is seen that we can eliminate the time taken for re-ordering completely if the window size is greater than 3 ms

No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems

Stream processing systems (SPSs) have been designed to process data streams in real-time, allowing organizations to analyze and act upon data on-the-fly, as it is generated. However, handling sensitive or personal data in these multilayered SPSs that distribute resources across sensor, fog, and cloud layers raises privacy concerns, as the data may be subject to unauthorized access and attacks that can violate user privacy, hence facing regulations such as the GDPR across the SPS layers. To address these issues, different privacy-preserving mechanisms (PPMs) are proposed to protect user privacy in SPSs. Yet, selecting and applying such PPMs in SPSs is challenging, since they must operate in real-time while tolerating little overhead. The multilayered nature of SPSs complicates privacy protection because each layer may confront different privacy threats, which must be addressed by specific PPMs. To overcome these challenges, we present Prinseps, our comprehensive privacy vision for SPSs. Towards this vision, we (1) identify critical privacy threats on different layers of the multilayered SPS, (2) evaluate the effectiveness of existing PPMs in addressing such threats, and (3) integrate privacy considerations into the decision-making processes of SPSs.Comment: Vision paper accepted to DEBS 202

A Garbled Circuit Accelerator for Arbitrary, Fast Privacy-Preserving Computation

Privacy and security have rapidly emerged as priorities in system design. One powerful solution for providing both is privacy-preserving computation, where functions are computed directly on encrypted data and control can be provided over how data is used. Garbled circuits (GCs) are a PPC technology that provide both confidential computing and control over how data is used. The challenge is that they incur significant performance overheads compared to plaintext. This paper proposes a novel garbled circuit accelerator and compiler, named HAAC, to mitigate performance overheads and make privacy-preserving computation more practical. HAAC is a hardware-software co-design. GCs are exemplars of co-design as programs are completely known at compile time, i.e., all dependence, memory accesses, and control flow are fixed. The design philosophy of HAAC is to keep hardware simple and efficient, maximizing area devoted to our proposed custom execution units and other circuits essential for high performance (e.g., on-chip storage). The compiler can leverage its program understanding to realize hardware's performance potential by generating effective instruction schedules, data layouts, and orchestrating off-chip events. In taking this approach we can achieve ASIC performance/efficiency without sacrificing generality. Insights of our approach include how co-design enables expressing arbitrary GC programs as streams, which simplifies hardware and enables complete memory-compute decoupling, and the development of a scratchpad that captures data reuse by tracking program execution, eliminating the need for costly hardware managed caches and tagging logic. We evaluate HAAC with VIP-Bench and achieve a speedup of 608$\times$ in 4.3mm$^2$ of area

Protecting private information in event processing systems

With the increasing number of sensors and smart objects in our daily use, the Internet of Things (IoT) becomes realistic. Thereby, modern applications like "e-health applications" or "smart homes" join our everyday life. These applications have the capability to detect situations of the real world and react to them. Complex Event Processing (CEP) systems can detect such occurring situations, which are in the form of event patterns, efficiently. Besides many benefits which such applications entail, it should not be forgotten that they have a huge impact on privacy. Therefore, it is important that a user has the possibility to decide on his own which complex information he wants to share and which not. This thesis presents a pattern-based access control algorithm which tries to conceal all privacy information in an event stream without destroying the public information. The idea is to reorder a specific set of events of the event stream in such a way that patterns which would result in privacy violations do not longer occur. The evaluation shows that a reorganization of events is possible in many cases without loss of public information.Das Internet der Dinge (InD) wird mit zunehmender Anzahl von Sensoren und "Smart Objekten" im täglichen Gebrauch immer realistischer. Dadurch erhalten neuartige Anwendungen wie "E-Health Applikationen" oder "Smart Homes" Einzug in unseren Alltag. Diese Anwendungen besitzen die Fähigkeit, Situationen aus der realen Welt zu erkennen und entsprechend darauf zu reagieren. Complex Event Processing (CEP) Systeme können solche auftretenden Situationen effizient in Form von Ereignismustern erkennen. Neben den vielen Vorteilen, die solche Anwendungen mit sich bringen, sollte jedoch nicht vergessen werden, dass sie einen immensen Eingriff in die Privatsphäre vornehmen. Daher ist es wichtig, Nutzern die Möglichkeit zu bieten selbst zu entscheiden, welche ihrer komplexen Informationen geteilt werden sollen und welche nicht. Diese Masterarbeit stellt einen musterbasierten Algorithmus vor, welcher versucht alle privaten Informationen in einem Ereignisstrom zu verschleiern ohne dabei die öffentlichen Informationen zu zerstören. Die Idee ist, ausgewählte Ereignisse des Ereignisstroms so umzustellen, dass bestimmte Muster, welche eine Verletzung der Privatsphäre zur Folge hätten, nicht mehr auftreten. Die Evaluierung zeigt, dass in vielen Fällen eine Umstellung von Ereignissen ohne Verlust von öffentlichen Informationen möglich ist

Turning Logs into Lumber: Preprocessing Tasks in Process Mining

Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.Comment: Accepted by EdbA'23 workshop, co-located with ICPM 202

Side channel attacks on smart home systems: A short overview

This paper provides an overview on side-channel attacks with emphasis on vulnerabilities in the smart home. Smart homes are enabled by the latest developments in sensors, communication technologies, internet protocols, and cloud services. The goal of a smart home is to have smart household devices collaborate without involvement of residents to deliver the variety of services needed for a higher quality of life. However, security and privacy challenges of smart homes have to be overcome in order to fully realize the smart home. Side channel attacks assume data is always leaking, and leakage of data from a smart home reveals sensitive information. This paper starts by reviewing side-channel attack categories, then it gives an overview on recent attack studies on different layers of a smart home and their malicious goals

Engage D2.6 Annual combined thematic workshops progress report (series 2)

The preparation, organisation and conclusions from the thematic challenge workshops, two ad hoc technical workshops, a technical session on data and a MET/ENV workshop held in 2019 and 2020 are described. Partly due to Covid-19, two of the 2020 thematic challenge workshops scheduled to take place at the end of 2020 were re-scheduled to January 2021. We also report on the preparation for these two workshops, while the conclusions will be included in the next corresponding deliverable

Prochlo: Strong Privacy for Analytics in the Crowd

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper

Exploring events and distributed representations of text in multi-document summarization

In this article, we explore an event detection framework to improve multi-document summarization. Our approach is based on a two-stage single-document method that extracts a collection of key phrases, which are then used in a centrality-as-relevance passage retrieval model. We explore how to adapt this single-document method for multi-document summarization methods that are able to use event information. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. To cope with the possible usage of different terms to describe the same event, we explore distributed representations of text in the form of word embeddings, which contributed to improve the summarization results. The proposed summarization methods are based on the hierarchical combination of single-document summaries. The automatic evaluation and human study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE-1 scores of 16% for TAC 2009 and of 17% for DUC 2007.info:eu-repo/semantics/submittedVersio

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others