Cetin Sahin

TaoStore: Overcoming Asynchronicity in Oblivious Data Storage

We consider oblivious storage systems hiding both the contents of the data as well as access patterns from an untrusted cloud provider. We target a scenario where multiple users from a trusted group (e.g., corporate employees) asynchronously access and edit potentially overlapping data sets through a trusted proxy mediating client-cloud communication. The main contribution of our paper is twofold. Foremost, we initiate the first formal study of asynchronicity in oblivious storage systems. We provide security definitions for scenarios where both client requests and network communication are asynchronous (and in fact, even adversarially scheduled). While security issues in ObliviStore (Stefanov and Shi, S&P 2013) have recently been surfaced, our treatment shows that also CURIOUS (Bindschaedler at al., CCS 2015), proposed with the exact goal of preventing these attacks, is insecure under asynchronous scheduling of network communication. Second, we develop and evaluate a new oblivious storage system, called Tree-based Asynchronous Oblivious Store, or TaoStore for short, which we prove secure in asynchronous environments. TaoStore is built on top of a new tree-based ORAM scheme that processes client requests concurrently and asynchronously in a non-blocking fashion. This results in a substantial gain in throughput, simplicity, and flexibility over previous systems.

Dragonfly: Cloud Assisted Peer-to-Peer Architecture for Multipoint Media Streaming Applications

Technology trends are not only transforming the hardware landscape of end-user devices but are also dramatically changing the types of software applications that are deployed on these devices. With the maturity of cloud computing during the past few years, users increasingly rely on networked applications that are deployed in the cloud. In particular, new applications will emerge where user interactions will be based on real-time continuous media streams instead of the traditional request-response types of interfaces. Furthermore, many of these applications will be multi-user streaming media based interactions instead of a single user interaction with an application. In this paper, we propose a geographic location-aware, hybrid, scalable cloud assisted peer-to-peer (P2P) architecture to support such applications that targets low administration cost, reduced bandwidth consumption, low latency, low initial investment cost and optimized resource usage. The main objective is to develop an efficient media delivery system that leverages locality. We propose a 3-layer novel architecture that uses at the core the cloud for application management, 2-tier edge cloud for supporting geo-dispersed user groups, and at the lowest level peer-to-peer dynamic overlays for locally clustered user groups. The proposed architecture manages multiple streaming sessions simultaneously and each streaming session is an independent entity. Our experiments on PlanetLab show that the dynamic construction and maintenance of delivering streams at both the user-level P2P overlay and edge cloud are indeed feasible and effective.

Privacy-preserving aggregation in life cycle assessment

Life cycle assessment (LCA) is the standard technique used to make a quantitative evaluation about the ecological sustainability of a product or service. The life cycle inventory (LCI) data sets that provide input to LCA computations can express essential information about the operation of a process or production step. As a consequence, LCI data are often regarded as confidential and are typically concealed through aggregation with other data sets. Despite the importance of privacy protection in publishing LCA studies, the community lacks a formal framework for managing private data, and no techniques exist for performing aggregation of LCI data sets that preserve the privacy of input data. However, emerging computational techniques known as “secure multiparty computation” enable data contributors to jointly compute numerical results without enabling any party to determine another party’s private data. In the proposed approach, parties who agree on a shared computation model, but do not trust one another and also do not trust a common third party, can collaboratively compute a weighted average of an LCA metric without sharing their private data with any other party. First, we formulate the LCA aggregation problem as an inner product over a foreground inventory model. Then, we show how LCA aggregations can be computed as the ratio of two secure sums. The protocol is useful when preparing LCA studies involving mutually competitive firms.

Understanding the Security Challenges of Oblivious Cloud Storage with Asynchronous Accesses

This demonstration introduces the database community to state-of-the-art cryptographic methods that ensure efficient oblivious access to cloud data. In particular, we explore oblivious storage systems which hide both the content of data and data access patterns from an untrusted cloud provider. The demo considers the popular and realistic setting where multiple users from a trusted group asynchronously access and edit potentially overlapping data sets through a trusted proxy. We present a detailed implementation of TaoStore (Sahin et al., S&P 2016), a new tree-based ORAM scheme that processes client requests concurrently and asynchronously in a non-blocking fashion, resulting in substantial gains in throughput, simplicity, and flexibility over previous systems. The demo is presented in the context of a pedagogical game, Guess the Access, which allows participants to play as an adversary trying to guess queries against TaoStore or ObliviStore (Stefanov and Shi, S&P 2013), a recent oblivious storage system which has been shown to leak access patterns. The proposed game will highlight the subtleties and intricacies that underlie the cryptographic methods used to design oblivious storage systems.

Privacy-Preserving Certification of Sustainability Metrics

Companies are often motivated to evaluate their environmental sustainability, and to make public pronouncements about their performance with respect to quantitative sustainability metrics. Public trust in these declarations is enhanced if the claims are certified by a recognized authority. Because accurate evaluations of environmental impacts require detailed information about industrial processes throughout a supply chain, protecting the privacy of input data in sustainability assessment is of paramount importance. We introduce a new paradigm, called privacy-preserving certification, that enables the computation of sustainability indicators in a privacy-preserving manner, allowing firms to be classified based on their individual performance without revealing sensitive information to the certifier, other parties, or the public. In this work, we describe different variants of the certification problem, highlight the necessary security requirements, and propose a provably-secure novel framework that performs the certification operations under the management of an authorized, yet untrusted, party without compromising confidential information.

Towards Practical Privacy-Preserving Life Cycle Assessment Computations

Life Cycle Assessment(LCA) is crucial for evaluating the ecological sustainability of a product or service, and the accurate evaluation of sustainability requires detailed and transparent information about industrial activities. However, such information is usually considered confidential and withheld from the public. In this paper, we present a rigorous study of privacy in the context of LCA. The main goal is to explore the privacy challenges in sustainability assessment considering the protection of trade secrets while increasing transparency of industrial activities. To overcome privacy concerns, we apply differential privacy to LCA computations considering the idiosyncratic features of LCA data. Our assessments on a specific real-life example show that it is possible to achieve privacy-preserving LCA computations without losing the utility of data completely.

LocBorg: Hiding Social Media User Location while Maintaining Online Persona

Social media streams analysis can reveal the characteristics of people who engage with or write about different topics. Recent works show that it is possible to reveal sensitive attributes (e.g., location, gender, ethnicity, political views, etc.) of individuals by analyzing their social media streams. Although, the prediction of a users sensitive attributes can be used to enhance the user experience in social media, revealing some attributes like the location could represent a threat on individuals. Users can obfuscate their location by posting about random topics linked to different locations. However, posting about random and sometimes contradictory topics that are not aligned with a users online persona and posts could negatively affect the followers interested in her profile. This paper represents our vision about the future of user privacy on social media. Users can locally deploy a cyborg, an artificial intelligent system that helps people to defend their privacy on social media. We propose LocBorg, a location privacy preserving cyborg that protects users by obfuscating their location while maintaining their online persona. LocBorg analyzes the social media streams and recommends topics to write about that are similar to a users topics of interest and aligned with the users online persona but linked to other locations.

Mind your Ps and Vs: A perspective on the challenges of big data management and privacy concerns

With large elastic and scalable infrastructures, the Cloud is the ideal storage repository for Big Data applications. Big Data is typically characterized by three Vs: Volume, Variety and Velocity. Supporting these properties raises significant challenges in a cloud setting, including partitioning for scale out; replication across data centers for fault-tolerance; significant latency overheads due to consistency requirements; efficient traversal needs due to high update and velocity; and continuous maintenance in the presence of large variety of data representations. Last but not least, the storage of private data necessitates the ability to efficiently execute queries in a privacy preserving manner (P), without revealing user access patterns. In this paper we highlight these challenges and illustrate sample state of the art solutions.