Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

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TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

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TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

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Symbolic Boolean manipulation with ordered binary-decision diagrams

Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.

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Physical unclonable functions for device authentication and secret key generation

Physical Unclonable Functions for Device Authentication and Secret Key Generation

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Towards Understanding Practical Randomness Beyond Noise: Differential Privacy and Mixup.

Method,integrated circuit,and computer program product for signal generator based device security

The scalability of cache coherence protocols is a significant challenge in multicore and other distributed shared memory systems. Traditional snoopy and directory-based coherence protocols are difficult to scale up to many-core systems because of the overhead of broadcasting and storing sharers for each cacheline. Tardis, a recently proposed coherence protocol, shows potential in solving the scalability problem, since it only requires O(logN) storage per cacheline for an N-core system and needs no broadcasting support. 
The original Tardis protocol, however, only supports the sequential consistency memory model. This limits its applicability in real systems since most processors today implement relaxed consistency models like Total Store Order (TSO). Tardis also incurs large network traffic overhead on some benchmarks due to an excessive number of renew messages. Furthermore, the original Tardis protocol has suboptimal performance when the program uses spinning to communicate between threads. 
In this paper, we address these downsides of Tardis protocol and make it significantly more practical. Specifically, we discuss the architectural, memory system and protocol changes required in order to implement TSO consistency model on Tardis, and prove that the modified protocol satisfies TSO. We also propose optimizations for better leasing policies and to handle program spinning. Evaluated on 20 benchmarks, optimized Tardis at 64 (256) cores can achieve average performance improvement of 15.8% (8.4%) compared to the baseline Tardis and 1% (3.4%) compared to the baseline directory protocol. Our optimizations also reduce the average network traffic by 4.3% (6.1%) compared to the baseline directory protocol. On this set of benchmarks, optimized Tardis improves on a fullmap directory protocol in the metrics of energy, performance and storage, while being simpler to implement.

Tardis 2.0: An Optimized Time Traveling Coherence Protocol.

Approach: Our approach to integrating access control with INS is based on the assumption that the interface between a resource discovery system and security infrastructure should not be hard. It is inefficient if a user (requestor) has to repeatedly iterate through lists of resources that he is prohibited from using while he searches for the most-optimal accessible resource. One only has to consider a scenario where a user is in an environment with several resources that are inaccesible to him to see how scalability becomes a major issue when adding security to resource discovery. If the resource discovery system has no knowledge about which resources the requestor can access, it could take tremendous computational effort to find an accessible resource. A better approach would be to give the resource discovery system knowledge about the access control lists (ACLs) that protect each of the resources and the access-control groups and capabilities of the requestor. A resource discovery system already knows about the service and performance characteristics of each resource it represents; an access control list is nothing more than an additional characteristic that defines the resource. The idea here is not to leave security entirely up to the resource discovery system, but, instead, to provide the discovery system with “hints” regarding the accessiblity of resources. Security is still enforced by an end-to-end proxy protocol (this protocol is described in our previous work [2]), but the sharing of access information enables the resource discovery system to find resources that are guaranteed to be accessible. Figure 1 shows a system level diagram of our entire security infrastructure. This summary focuses on integrating acess control into INS. INS provides users with a layer of abstraction so that applications do not need to know the availability or exact name of the resource for which they are looking. We extend INS to provide access-controlled resource discovery in two main ways: • Dynamic maintenance of ACLs in INS. We implement a real-time maintenance of access control lists (ACLs) in the INS name resolvers in order to give INS knowledge about how each resource should be protected. • User authorization. We introduce a certificate-based authorization step during the resolution of an INS request in order for INS to know the identity and privileges of a requestor.

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Access-Controlled Resource Discovery

NWe introduce the Execution Migration Machine (EM 2 ), a novel data -centric multicore memory system architectu re based on computation migration. Unlike traditional distributed memory multicores, which rely on complex cache coherence protocols to move the data to the core where the computation is taking place, our scheme always moves the computation to the core whe re the data resides. By doing away with the cache coherence protocol, we are able to boost the effectiveness of per-core caches while drastically reducing hardware complexity. To evaluate the potential of EM 2 architectures, we developed a series of PIN/Gra phite-based models of an EM 2 multicore with 64 x86 cores and, under some simplifying assumptions (a timing model restricted to data memory performance, no instruction cache modeling, high -bandwidth fixed -latency interconnect allowing concurrent migrations) , compared them against corresponding directory -based cache -coherent architecture models. We justify our assumptions and show that our conclusions are valid even if our assumptions are removed. Experimental results on a range of SPLASH -2 and PARSEC benchma rks indicate that EM

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Srinivas Devadas

Papers

Towards Understanding Practical Randomness Beyond Noise: Differential Privacy and Mixup.

Method,integrated circuit,and computer program product for signal generator based device security

Tardis 2.0: An Optimized Time Traveling Coherence Protocol.

Access-Controlled Resource Discovery

Instruction-Level Execution Migration