Recent compilers allow a general-purpose program that handles private data to be translated into a secure distributed implementation of the corresponding functionality. The resulting program is guaranteed to provably protect private data using secure multi-party computation techniques. The goals of such compilers are generality, usability, and efficiency, but the complete set of features of a modern programming language has not been supported by the existing compilers. In particular, recent compilers PICCO and the two-party ANSI C compiler strive to translate any C program into its secure multi-party implementation, but currently lack support for pointers and dynamic memory allocation, which are important components of many C programs. In this work, we mitigate the limitation and add support for pointers to private data and dynamic memory allocation to the PICCO compiler, enabling it to handle a more diverse set of programs over private data. Because doing so opens up a new design space, we investigate the use of pointers to private data (with known as well as private locations stored in them) in programs and report our findings. Aside from dynamic memory allocation, we examine other important topics associated with pointer use such as reference by pointer/address, casting, and building data structures in the context of secure multi-party computation. This results in enabling the compiler to automatically translate a user program that uses pointers to private data into its distributed implementation that provably protects private data throughout the computation. We empirically evaluate the constructions and report on the performance of representative programs.
Malware and code-reuse attacks are the most significant threats to current systems operation. Solutions de- veloped to countermeasure them have their weaknesses exploited by attackers through sandbox evasion and anti-debug crafting. To address such weaknesses, we propose a framework that relies on modern processors branch monitor feature to allow us to analyze malware while reducing evasion effects. The use of hardware- assistance aids in increasing stealthiness, a key feature for debuggers, since modern software (malicious or benign) may be anti-analysis armored. We achieve stealthier code execution control by using the branch monitor hardwares inherent interrupt capabilities, keeping the code under execution intact. Previous work on branch monitoring have already addressed the ROP attack problem, but require code injection and/or are limited in their capture window size. Therefore, we also propose an ROP detector without these limitations.
Private set intersection (PSI) allows two parties to compute the intersection of their sets without revealing any information about items that are not in the intersection. It is one of the best studied applications of secure computation and many PSI protocols have been proposed. However, the variety of existing PSI protocols makes it difficult to identify the solution that performs best in a respective scenario, especially since they were not compared in the same setting. In addition, existing PSI protocols are several orders of magnitude slower than an insecure naive hashing solution which is used in practice. In this work, we review the progress made on PSI protocols and give an overview of existing protocols in various security models. We then focus on PSI protocols that are secure against semi-honest adversaries and take advantage of the most recent efficiency improvements in OT extension and propose significant optimizations to previous PSI protocols and to suggest a new PSI protocol whose run-time is superior to that of existing protocols. We compare the performance of the protocols both theoretically and experimentally, by implementing all protocols on the same platform, give recommendations on which protocol to use in a particular setting, and evaluate the progress on PSI protocols by comparing them to the currently employed insecure naive hashing protocol. We demonstrate the feasibility of our new PSI protocol by processing two sets with a billion elements each.
Hardware Performance Counters (HPCs) are useful artifacts for evaluating the performance of software implementations. Recently, HPCs have been made more convenient to use without requiring explicit kernel patches or superuser privileges. However, we highlight that the information revealed by HPCs can be also exploited to attack standard implementations of public-key algorithms. In particular we analyze the vulnerability due to the event branch-miss leaked via the HPCs during execution of the target ciphers. We present an iterative attack which target the key-bits of 1024-bit RSA and 256-bit ECC where in offline phase, the systems underlying branch predictor is approximated by a theoretical predictor in literature. Subsimulations are performed corresponding to each bit guess to classify the message-space into distinct partitions based on the event branch misprediction and the target key bit value. In online phase, branch mispredictions obtained from the HPCs on target system reveals the secret key bits. We also theoretically prove that the probability of success of the attack is equivalent to the accurate modeling of the theoretical predictor to the underlying system predictor. In addition, we propose an improved version of the attack which requires lesser branch misprediction traces from the HPCs to recover the secret. Experimentations using both the attack strategies have been provided on Intel Core 2 Duo, Core i3 and Core i5 platforms for 1024-bit RSA and 256-bit prime secp256r1 curve. The attack can successfully reveal the exponent bits and thus seeks attention to model secure branch predictors such that it inherently prevents information leakage.
We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records, but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users; via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.