40th IEEE Symposium on
Security and Privacy

Accepted Papers

Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization
Steven H. H. Ding (McGill University), Benjamin C. M. Fung (McGill University), Philippe Charland (Defence R&D; Canada - Valcartier, Canada)
Reverse engineering is a manually intensive but necessary technique for understanding the inner workings of new malware, finding vulnerabilities in existing systems, and detecting patent infringements in released software. An assembly clone search engine facilitates the work of reverse engineers by identifying those duplicated or known parts. However, it is challenging to design a robust clone search engine, since there exist various compiler optimization options and code obfuscation techniques that make logically similar assembly functions appear to be very different.

A practical clone search engine relies on a robust vector representation of assembly code. However, the existing clone search approaches, which rely on a manual feature engineering process to form a feature vector for an assembly function, fail to consider the relationships between features and identify those unique patterns that can statistically distinguish assembly functions. To address this problem, we propose to jointly learn the lexical semantic relationships and the vector representation of assembly functions based on assembly code. We have developed an assembly code representation learning model \emph{Asm2Vec}. It only needs assembly code as input and does not require any prior knowledge such as the correct mapping between assembly functions. It can find and incorporate rich semantic relationships among tokens appearing in assembly code. We conduct extensive experiments and benchmark the learning model with state-of-the-art static and dynamic clone search approaches. We show that the learned representation is more robust and significantly outperforms existing methods against changes introduced by obfuscation and optimizations.
Attack Directories, Not Caches: Side Channel Attacks in a Non-Inclusive World
Mengjia Yan (University of Illinois at Urbana Champaign), Read Sprabery (University of Illinois at Urbana Champaign), Bhargava Gopireddy (University of Illinois at Urbana Champaign), Christopher Fletcher (University of Illinois at Urbana Champaign), Roy Campbell (University of Illinois at Urbana Champaign), Josep Torrellas (University of Illinois at Urbana Champaign)
Although clouds have strong virtual memory isolation guarantees, cache attacks stemming from shared caches have proved to be a large security problem. However, despite the past effectiveness of cache attacks, their viability has recently been called into question on modern systems, due to trends in cache hierarchy design moving away from inclusive cache hierarchies.

In this paper, we reverse engineer the structure of the directory in a sliced, non-inclusive cache hierarchy, and prove that the directory can be used to bootstrap conflict-based cache attacks on the last-level cache. We design the first cross-core Prime+Probe attack on non-inclusive caches. This attack works with minimal assumptions: the adversary does not need to share any virtual memory with the victim, nor run on the same processor core. We also show the first high-bandwidth Evict+Reload attack on the same hardware. We demonstrate both attacks by extracting key bits during RSA operations in GnuPG on a state-of-the-art non-inclusive Intel Skylake-X server.
Blind Certificate Authorities
Liang Wang (UW Madison), Gilad Asharov (Cornell Tech), Rafael Pass (Cornell Tech), Thomas Ristenpart (Cornell Tech), Abhi Shelat (Northeastern University)
We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key.

To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address to a public key of her choosing without ever revealing ``alice'' to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.
Breaking LTE on Layer Two
David Rupprecht (Ruhr-University Bochum), Katharina Kohls (Ruhr-University Bochum), Thorsten Holz (Ruhr-University Bochum), Christina Pöpper (New York University Abu Dhabi)
Long Term Evolution (LTE) is the latest mobile communication standard and has a pivotal role in our information society: LTE combines performance goals with modern security mechanisms and serves casual use cases as well as critical infrastructure and public safety communications. Both scenarios are demanding towards a resilient and secure specification and implementation of LTE, as outages and open attack vectors potentially lead to severe risks. Previous work on LTE protocol security identified crucial attack vectors for both the physical (layer one) and network (layer three) layers. Data link layer (layer two) protocols, however, remain a blind spot in existing LTE security research.

In this paper, we present a comprehensive layer two security analysis and identify three attack vectors. These attacks impair the confidentiality and/or privacy of LTE communication. More specifically, we first present a passive identity mapping attack that matches volatile radio identities to longer lasting network identities, enabling us to identify users within a cell and serving as a stepping stone for follow-up attacks. Second, we demonstrate how a passive attacker can abuse the resource allocation as a side channel to perform website fingerprinting that enables the attacker to learn the websites a user accessed. Finally, we present the A LTE R attack that exploits the fact that LTE user data is encrypted in counter mode (AES-CTR) but not integrity protected, which allows us to modify the message payload. As a proof-of-concept demonstration, we show how an active attacker can redirect DNS requests and then perform a DNS spoofing attack. As a result, the user is redirected to a malicious website. Our experimental analysis demonstrates the real-world applicability of all three attacks and emphasizes the threat of open attack vectors on LTE layer two protocols.
Dangerous Skills: Understanding and Mitigating Security Risks of Voice-Controlled Third-Party Functions on Virtual Personal Assistant Systems
Nan Zhang (Indiana University, Bloomington), Xianghang Mi (Indiana University, Bloomington), Xuan Feng (Indiana University, Bloomington; Beijing Key Laboratory of IOT Information Security Technology, Institute of Information Engineering, CAS, China), XiaoFeng Wang (Indiana University, Bloomington), Yuan Tian (University of Virginia), Feng Qian (Indiana University, Bloomington)
Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication (from the user to the VPA). A new authentication challenge, from the VPA service to the user, has emerged with the rapid growth of the VPA ecosystem, which allows a third party to publish a function (called skill) for the service and therefore can be exploited to spread malicious skills to a large audience during their interactions with smart speakers like Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. We discovered two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., ``open capital one''), using a malicious skill with a similarly pronounced name (e.g., ``capital won'') or a paraphrased name (e.g., ``capital one please'') to hijack the voice command meant for a legitimate skill (e.g., ``capital one''), and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill during the user's conversation with the service to steal her personal information. These attacks aim at the way VPAs work or the user's misconceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings has already been acknowledged by Amazon and Google, and further evidenced by the risky skills found on Alexa and Google markets by the new squatting detector we built. We further developed a technique that automatically captures an ongoing masquerading attack and demonstrated its efficacy.
Data Recovery on Encrypted Databases with k-Nearest Neighbor Query Leakage
Evgenios M. Kornaropoulos (Brown University), Charalampos Papamanthou (University of Maryland), Roberto Tamassia (Brown University)
Recent works by Kellaris et al. (CCS'16) and Lacharite et al. (SP'18) demonstrated attacks of data recovery for encrypted databases that support rich queries such as range queries. In this paper, we develop the first data recovery attacks on encrypted databases supporting one-dimensional k-nearest neighbor (k-NN) queries, which are widely used in spatial data management. Our attacks exploit a generic k-NN query leakage profile: the attacker observes the identifiers of matched records. We consider both unordered responses, where the leakage is a set, and ordered responses, where the leakage is a k-tuple ordered by distance from the query point.

As a first step, we perform a theoretical feasibility study on exact reconstruction, i.e., recovery of the exact plaintext values of the encrypted database. For ordered responses, we show that exact reconstruction is feasible if the attacker has additional access to some auxiliary information that is normally not available in practice. For unordered responses, we prove that exact reconstruction is impossible due to the infinite number of valid reconstructions. As a next step, we propose practical and more realistic approximate reconstruction attacks so as to recover an approximation of the plaintext values. For ordered responses, we show that after observing enough query responses, the attacker can approximate the client's encrypted database with considerable accuracy. For unordered responses we characterize the set of valid reconstructions as a convex polytope in a k-dimensional space and present a rigorous attack that reconstructs the plaintext database with bounded approximation error.

As multidimensional spatial data can be efficiently processed by mapping it to one dimension via Hilbert curves, we demonstrate our approximate reconstruction attacks on privacy-sensitive geolocation data. Our experiments on real-world datasets show that our attacks reconstruct the plaintext values with relative error ranging from 2.9% to 0.003%.
Differentially Private Model Publishing for Deep Learning
Lei Yu (Georgia Institute of Technology), Ling Liu (Georgia Institute of Technology), Calton Pu (Georgia Institute of Technology), Mehmet Emre Gursoy (Georgia Institute of Technology), Stacey Truex (Georgia Institute of Technology)
Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for their success. However, when the training datasets are crowdsourced from individuals and contain sensitive information, the model parameters may encode private information and bear the risks of privacy leakage. The recent growing trend of the sharing and publishing of pre-trained models further aggravates such privacy risks. To tackle this problem, we propose a differentially private approach for training neural networks. Our approach includes several new techniques for optimizing both privacy loss and model accuracy. We employ a generalization of differential privacy called concentrated differential privacy(CDP), with both a formal and refined privacy loss analysis on two different data batching methods. We implement a dynamic privacy budget allocator over the course of training to improve model accuracy. Extensive experiments demonstrate that our approach effectively improves privacy loss accounting, training efficiency and model quality under a given privacy budget.
Hard Drive of Hearing: Disks that Eavesdrop with a Synthesized Microphone
Andrew Kwong (University of Michigan), Wenyuan Xu (Zhejiang University), Kevin Fu (University of Michigan)
Security conscious individuals may take considerable measures to disable sensors in order to protect their privacy. However, they often overlook the cyberphysical attack surface exposed by devices that were never designed to be sensors in the first place. Our research demonstrates that the mechanical components in magnetic hard disk drives behave as microphones with sufficient precision to extract and parse human speech. These unintentional microphones sense speech with high enough fidelity for the Shazam service to recognize a song recorded through the hard drive. This proof of concept attack sheds light on the possibility of invasion of privacy even in absence of traditional sensors. We also present defense mechanisms, such as the use of ultrasonic aliasing, that can mitigate acoustic eavesdropping by synthesized microphones in hard disk drives.
How Well Do My Results Generalize? Comparing Security and Privacy Survey Results from MTurk, Web, and Telephone Samples
Elissa M. Redmiles (University of Maryland), Sean Kross (University of California San Diego), Michelle L. Mazurek (University of Maryland)
Security and privacy researchers often rely on data collected from Amazon Mechanical Turk (MTurk) to evaluate security tools, to understand users' privacy preferences and to measure online behavior. Yet, little is known about how well Turkers' survey responses and performance on security- and privacy-related tasks generalizes to a broader population. This paper takes a first step toward understanding the generalizability of security and privacy user studies by comparing users' self-reports of their security and privacy knowledge, past experiences, advice sources, and behavior across samples collected using MTurk (n=480), a census-representative web-panel (n=428), and a probabilistic telephone sample (n=3,000) statistically weighted to be accurate within 2.7% of the true prevalence in the U.S.

Surprisingly, the results suggest that: (1) MTurk responses regarding security and privacy experiences, advice sources, and knowledge are more representative of the U.S. population than are responses from the census-representative panel; (2) MTurk and general population reports of security and privacy experiences, knowledge, and advice sources are quite similar for respondents who are younger than 50 or who have some college education; and (3) respondents' answers to the survey questions we ask are stable over time and robust to relevant, broadly-reported news events. Further, differences in responses cannot be ameliorated with simple demographic weighting, possibly because MTurk and panel participants have more internet experience compared to their demographic peers. Together, these findings lend tempered support for the generalizability of prior crowdsourced security and privacy user studies; provide context to more accurately interpret the results of such studies; and suggest rich directions for future work to mitigate experience- rather than demographic-related sample biases.
Perun: Virtual Payment Hubs over Cryptocurrencies
Stefan Dziembowski (University of Warsaw), Lisa Eckey (TU Darmstadt), Sebastian Faust (TU Darmstadt), Daniel Malinowski (University of Warsaw)
Payment channels emerged recently as an efficient method for performing cheap micropayments in cryptocurrencies. In contrast to traditional on-chain transactions, payment channels have the advantage that they allow for nearly unlimited number of transactions between parties without involving the blockchain. In this work, we introduce Perun, an off-chain channel system that offers a new method for connecting channels that is more efficient than the existing technique of ``routing transactions'' over multiple channels. To this end, Perun introduces a technique called ``virtual payment channels'' that avoids involvement of the intermediary for each individual payment. In this paper we formally model and prove security of this technique in the case of one intermediary, who can be viewed as a ``payment hub'' that has direct channels with several parties. Our scheme works over any cryptocurrency that provides Turing-complete smart contracts. As a proof of concept, we implemented Perun's smart contracts in Ethereum.
PrivKV: Key-Value Data Collection with Local Differential Privacy
Qingqing Ye (Renmin University of China), Haibo Hu (Hong Kong Polytechnic University), Xiaofeng Meng (Renmin University of China), Huadi Zheng (Hong Kong Polytechnic University)
Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach PrivKV within the same "perturbation-calibration" paradigm as existing LDP techniques. To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions PrivKVM and PrivKVM+ that can gradually improve the estimation results through a series of iterations. An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement. We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.
Razzer: Finding Kernel Race Bugs through Fuzzing
Dae R. Jeong (KAIST), Kyungtae Kim (Purdue University), Basavesh Shivakumar (Purdue University), Byoungyoung Lee (Seoul National University, Purdue University), Insik Shin (KAIST)
A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.
Resident Evil: Understanding Residential IP Proxy as a Dark Service
Xianghang Mi (Indiana University Bloomington), Xuan Feng (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Baojun Liu (Tsinghua University), XiaoFeng Wang (Indiana University Bloomington), Feng Qian (Indiana University Bloomington), Zhou Li (IEEE member), Sumayah Alrwais (King Saud University), Limin Sun (Institute of Information Engineering, CAS), Ying Liu (Tsinghua University)
Abstract-An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them.

In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.
Short Text, Large Effect: Measuring the Impact of User Reviews on Android App Security & Privacy
Duc Cuong Nguyen (CISPA, Saarland University), Erik Derr (CISPA, Saarland University), Michael Backes (CISPA Helmholtz Center i.G.), Sven Bugiel (CISPA Helmholtz Center i.G.)
Application markets streamline the end-users' task of finding and installing applications. They also form an immediate communication channel between app developers and their end-users in form of app reviews, which allow users to provide developers feedback on their apps. However, it is unclear to which extent users employ this channel to point out their security and privacy concerns about apps, about which aspects of apps users express concerns, and how developers react to such security- and privacy-related reviews.

In this paper, we present the first study of the relationship between end-user reviews and security- & privacy-related changes in apps. Using natural language processing on 4.5M user reviews for the top 2,583 apps in Google Play, we identified 5,527 security and privacy relevant reviews (SPR). For each app version mentioned in the SPR, we use static code analysis to extract permission-protected features mentioned in the reviews. We successfully mapped SPRs to privacy-related changes in app updates in 60.77% of all cases. Using exploratory data analysis and regression analysis we are able to show that preceding SPR are a significant factor for predicting privacy-related app updates, indicating that user reviews in fact lead to privacy improvements of apps. Our results further show that apps that adopt runtime permissions receive a significantly higher number of SPR, showing that runtime permissions put privacy-jeopardizing actions better into users' minds. Further, we can attribute about half of all privacy-relevant app changes exclusively to third-party library code. This hints at larger problems for app developers to adhere to users' privacy expectations and markets' privacy regulations.

Our results make a call for action to make app behavior more transparent to users in order to leverage their reviews in creating incentives for developers to adhere to security and privacy best practices, while our results call at the same time for better tools to support app developers in this endeavor.
Simple High-Level Code for Cryptographic Arithmetic - With Proofs, Without Compromises
Andres Erbsen (Massachusetts Institute of Technology), Jade Philipoom (Massachusetts Institute of Technology), Jason Gross (Massachusetts Institute of Technology), Robert Sloan (Massachusetts Institute of Technology), Adam Chlipala (Massachusetts Institute of Technology)
We introduce a new approach for implementing cryptographic arithmetic in short high-level code with machine-checked proofs of functional correctness. We further demonstrate that simple partial evaluation is sufficient to transform into the fastest-known C code, breaking the decades-old pattern that the only fast implementations are those whose instruction-level steps were written out by hand.

These techniques were used to build an elliptic-curve library that achieves competitive performance for 80 prime fields and multiple CPU architectures, showing that implementation and proof effort scales with the number and complexity of conceptually different algorithms, not their use cases. As one outcome, we present the first verified high-performance implementation of P-256, the most widely used elliptic curve. implementations from our library were included in BoringSSL to replace existing specialized code, for inclusion in several large deployments for Chrome, Android, and CloudFlare.
SoK: Sanitizing for Security
Dokyung Song (University of California, Irvine), Julian Lettner (University of California, Irvine), Prabhu Rajasekaran (University of California, Irvine), Yeoul Na (University of California, Irvine), Stijn Volckaert (University of California, Irvine), Per Larsen (University of California, Irvine), Michael Franz (University of California, Irvine)
The C and C++ programming languages are notoriously insecure yet remain indispensable. Developers therefore resort to a multi-pronged approach to find security issues before adversaries. These include manual, static, and dynamic program analysis. Dynamic bug finding tools-henceforth "sanitizers"-can find bugs that elude other types of analysis because they observe the actual execution of a program, and can therefore directly observe incorrect program behavior as it happens.
A vast number of sanitizers have been prototyped by academics and refined by practitioners. We provide a systematic overview of sanitizers with an emphasis on their role in finding security issues. Specifically, we taxonomize the available tools and the security vulnerabilities they cover, describe their performance and compatibility properties, and highlight various trade-offs.
SoK: Security Evaluation of Home-Based IoT Deployments
Omar Alrawi (Georgia Institute of Technology), Chaz Lever (Georgia Institute of Technology), Manos Antonakakis (Georgia Institute of Technology), Fabian Monrose (University of North Carolina at Chapel Hill)
Home-based IoT devices have a bleak reputation regarding their security practices. On the surface, the insecurities of IoT devices seem to be caused by integration problems that may be addressed by simple measures, but this work finds that to be a naive assumption. The truth is, IoT deployments, at their core, utilize traditional compute systems, such as embedded, mobile, and network. These components have many unexplored challenges such as the effect of over-privileged mobile applications on embedded devices.

Our work proposes a methodology that researchers and practitioners could employ to analyze security properties for home-based IoT devices. We systematize the literature for home-based IoT using this methodology in order to understand attack techniques, mitigations, and stakeholders. Further, we evaluate \numDevices devices to augment the systematized literature in order to identify neglected research areas. To make this analysis transparent and easier to adapt by the community, we provide a public portal to share our evaluation data and invite the community to contribute their independent findings.
SoK: The Challenges, Pitfalls, and Perils of Using Hardware Performance Counters for Security
Sanjeev Das (University of North Carolina at Chapel Hill), Jan Werner (University of North Carolina at Chapel Hill), Manos Antonakakis (Georgia Institute of Technology), Michalis Polychronakis (Stony Brook University), Fabian Monrose (University of North Carolina at Chapel Hill)
Hardware Performance Counters (HPCs) have been available in processors for more than a decade. These counters can be used to monitor and measure events that occur at the CPU level. Modern processors provide hundreds of hardware events that can be monitored, and with each new processor architecture more are added. Yet, there has been little in the way of systematic studies on how performance counters can best be utilized to accurately monitor events in real-world settings. Especially when it comes to the use of HPCs for security applications, measurement imprecisions or incorrect assumptions regarding the measured values can undermine the offered protection.

To shed light on this issue, we embarked on a year-long effort to (i) study the best practices for obtaining accurate measurement of events using performance counters, (ii) understand the challenges and pitfalls of using HPCs in various settings, and (iii) explore ways to obtain consistent and accurate measurements across different settings and architectures. Additionally, we then empirically evaluated the way HPCs have been used throughout a wide variety of papers. Not wanting to stop there, we explored whether these widely used techniques are in fact obtaining performance counter data correctly. As part of that assessment, we (iv) extended the seminal work of Weaver and McKee from almost 10 years ago on non-determinism in HPCs, and applied our findings to 56 papers across various application domains.

In that follow-up study, we found the acceptance of HPCs in security applications is in stark contrast to other application areas - especially in the last five years. Given that, we studied an additional representative set of 41 works from the security literature that rely on HPCs, to better elucidate how the intricacies we discovered can impact the soundness and correctness of their approaches and conclusions. Toward that goal, we (i) empirically evaluated how failure to accommodate for various subtleties in the use of HPCs can undermine the effectiveness of security applications, specifically in the case of exploit prevention and malware detection. Lastly, we showed how (ii) an adversary can manipulate HPCs to bypass certain security defenses.
Spectre Attacks: Exploiting Speculative Execution
Paul Kocher (Independent (, Jann Horn (Google Project Zero), Anders Fogh (G DATA Advanced Analytics), Daniel Genkin (University of Pennsylvania and University of Maryland), Daniel Gruss (Graz University of Technology), Werner Haas (Cyberus Technology), Mike Hamburg (Rambus, Cryptography Research Division), Moritz Lipp (Graz University of Technology), Stefan Mangard (Graz University of Technology), Thomas Prescher (Cyberus Technology), Michael Schwarz (Graz University of Technology), Yuval Yarom (University of Adelaide and Data61)
Modern processors use branch prediction and speculative execution to maximize performance. For example, if the destination of a branch depends on a memory value that is in the process of being read, CPUs will try to guess the destination and attempt to execute ahead. When the memory value finally arrives, the CPU either discards or commits the speculative computation. Speculative logic is unfaithful in how it executes, can access the victim's memory and registers, and can perform operations with measurable side effects. Spectre attacks involve inducing a victim to speculatively perform operations that would not occur during correct program execution and which leak the victim's confidential information via a side channel to the adversary. This paper describes practical attacks that combine methodology from side channel attacks, fault attacks, and return-oriented programming that can read arbitrary memory from the victim's process. More broadly, the paper shows that speculative execution implementations violate the security assumptions underpinning numerous software security mechanisms, including operating system process separation, containerization, just-in-time (JIT) compilation, and countermeasures to cache timing and side-channel attacks. These attacks represent a serious threat to actual systems since vulnerable speculative execution capabilities are found in microprocessors from Intel, AMD, and ARM that are used in billions of devices. While makeshift processor-specific countermeasures are possible in some cases, sound solutions will require fixes to processor designs as well as updates to instruction set architectures (ISAs) to give hardware architects and software developers a common understanding as to what computation state CPU implementations are (and are not) permitted to leak.
Towards Practical Differentially Private Convex Optimization
Roger Iyengar (Carnegie Mellon University), Joseph P. Near (University of California, Berkeley), Dawn Song (University of California, Berkeley), Om Thakkar (Boston University), Abhradeep Thakurta (University of California, Santa Cruz), Lun Wang (Peking University)
Building useful predictive models often involves learning from sensitive data. Training models with differential privacy can guarantee the privacy of such sensitive data. For convex optimization tasks, several differentially private algorithms are known, but none has yet been deployed in practice. In this work, we make two major contributions towards practical differentially private convex optimization. First, we present Approximate Minima Perturbation, a novel algorithm that can leverage any off-the-shelf optimizer. We show that it can be employed without any hyperparameter tuning, thus making it an attractive technique for practical deployment. Second, we perform an extensive empirical evaluation of the state-of-the-art algorithms for differentially private convex optimization, on a range of publicly available benchmark datasets, and real-world datasets obtained through an industrial collaboration. We release open-source implementations of all the differentially private convex optimization algorithms considered, and benchmarks on as many as nine public datasets, four of which are high-dimensional.
Why Does Your Data Leak? Uncovering the Data Leakage in Cloud from Mobile Apps
Chaoshun Zuo (The Ohio State University), Zhiqiang Lin (The Ohio State University), Yinqian Zhang (The Ohio State University)
Increasingly, more and more mobile applications (apps for short) are using the cloud as the back-end, in particular the cloud APIs, for data storage, data analytics, message notification, and monitoring. Unfortunately, we have recently witnessed massive data leaks from the cloud, ranging from personally identifiable information to corporate secrets. In this paper, we seek to understand why such significant leaks occur and design tools to automatically identify them. To our surprise, our study reveals that lack of authentication, misuse of various keys (e.g., normal user keys and superuser keys) in authentication, or misconfiguration of user permissions in authorization are the root causes. Then, we design a set of automated program analysis techniques including obfuscation-resilient cloud API identification and string value analysis, and implement them in a tool called LeakScope to identify the potential data leakage vulnerabilities from mobile apps based on how the cloud APIs are used. Our evaluation with over 1.6 million mobile apps from the Google Play Store has uncovered 15, 098 app servers managed by mainstream cloud providers such as Amazon, Google, and Microsoft that are subject to data leakage attacks. We have made responsible disclosure to each of the cloud service providers, and they have all confirmed the vulnerabilities we have identified and are actively working with the mobile app developers to patch their vulnerable services.