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

In this article, we introduce a single-chip secure processor called Aegis. In addition to supporting mechanisms to authenticate the platform and software, our processor incorporates mechanisms to protect the integrity and privacy of applications from physical attacks as well as software attacks. Therefore, physically secure systems can be built using this processor. Two key primitives, physical unclonable functions (PUFs) and off-chip memory protection, enable the physical security of our system. These primitives can also be easily applied to other secure computing systems to enhance their security.

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Aegis: A Single-Chip Secure Processor

An approach to test-pattern generation for synchronous sequential circuits is presented. The deterministic sequential test-generation algorithm, based on extensions to the PODEM justification algorithm, is effective for midsized sequential circuits and can be used in conjunction with an incomplete scan design approach to generate tests for very large sequential circuits. Tests for finite-state machines with a large number of states have been successfully generated using reasonable amounts of CPU time and close-to-maximum possible fault coverages have been obtained. For very large sequential circuits, an incomplete scan-design approach to test generation has been developed. The deterministic test generation algorithm is again used to generate test for faults in the modified circuit. All irredundant faults can be detected as in the complete scan design case, but at significantly less area and performance cost. The length of the test sequences for the faults can be bounded by a prescribed value-in general, a tradeoff exists between the number of memory elements required to be made scannable and the maximum allowed length of the test sequence. >

Test generation for sequential circuits

We introduce Slender PUF protocol, an efficient and secure method to authenticate the responses generated from a Strong Physical Unclonable Function (PUF). The new method is lightweight, and suitable for energy constrained platforms such as ultra-low power embedded systems for use in identification and authentication applications. The proposed protocol does not follow the classic paradigm of exposing the full PUF responses (or a transformation of the full string of responses) on the communication channel. Instead, random subsets of the responses are revealed and sent for authentication. The response patterns are used for authenticating the prover device with a very high probability. We perform a thorough analysis of the method's resiliency to various attacks which guides adjustment of our protocol parameters for an efficient and secure implementation. We demonstrate that Slender PUF protocol, if carefully designed, will be resilient against all known machine learning attacks. In addition, it has the great advantage of an inbuilt PUF error tolerance. Thus, Slender PUF protocol is lightweight and does not require costly additional error correction, fuzzy extractors, and hash modules suggested in most previously known PUF-based robust authentication techniques. The low overhead and practicality of the protocol are confirmed by a set of hardware implementation and evaluations.

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Slender PUF Protocol: A Lightweight, Robust, and Secure Authentication by Substring Matching

This paper considers encrypted computation where the user specifies encrypted inputs to an untrusted program, and the server computes on those encrypted inputs. To this end we propose a secure processor architecture, called Ascend, that guarantees privacy of data when arbitrary programs use the data running in a cloud-like environment (e.g., an untrusted server running an untrusted software stack).The key idea to guarantee privacy is obfuscated instruction execution; Ascend does not disclose what instruction is being run at any given time, be it an arithmetic instruction or a memory instruction. Periodic accesses to external instruction and data memory are performed through an Oblivious RAM (ORAM) interface to prevent leakage through memory access patterns. We evaluate the processor architecture on SPEC benchmarks running on encrypted data and quantify overheads.

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A secure processor architecture for encrypted computation on untrusted programs

A group of devices are fabricated based on a common design, each device having a corresponding plurality of measurable characteristics that is unique in the group to that device, each device having a measurement module for measuring the measurable characteristics. Authentication of one of the group of devices is enabled by selective measurement of one or more of the plurality of measurable characteristics of the device.

Srinivas Devadas

Papers

Aegis: A Single-Chip Secure Processor

Test generation for sequential circuits

Slender PUF Protocol: A Lightweight, Robust, and Secure Authentication by Substring Matching

A secure processor architecture for encrypted computation on untrusted programs

Authentication of integrated circuits