When collecting information, local differential privacy (LDP) alleviates privacy concerns of users, as users' private information is randomized before being sent to the central aggregator. However, LDP results in loss of utility due to the amount of noise that is added. To address this issue, recent work introduced an intermediate server and with the assumption that this intermediate server did not collude with the aggregator. Using this trust model, one can add less noise to achieve the same privacy guarantee; thus improving the utility. In this paper, we investigate this multiple-party setting of LDP. We first analyze the threat model and identify potential adversaries. We then make observations about existing approaches and propose new techniques that achieve a better privacy-utility tradeoff than existing ones. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
DP by Trusted Hardware In this approach, a trusted hardware (e.g., SGX) is utilized to collect data, tally the data, and add the noise within the protected hardware. The result is then sent to the analyst. Google propose Prochlo @cite_29 that uses SGX. Note that the trusted hardware can be run by the server. Thus @cite_38 and @cite_1 designed oblivious DP algorithms to overcome the threat of side information (memory access pattern may be related to the underlying data). These proposals assume the trusted hardware is safe to use. However, using trusted hardware has potential risks (e.g., @cite_23 ). This paper considers the setting without trusted hardware.
@cite_38 @cite_29 @cite_1 @cite_23
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The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. With ESA, the privacy of monitored users' data is guaranteed by its processing in a three-step pipeline. First, the data is encoded to control scope, granularity, and randomness. Second, the encoded data is collected in batches subject to a randomized threshold, and blindly shuffled, to break linkability and to ensure that individual data items get "lost in the crowd" of the batch. Third, the anonymous, shuffled data is analyzed by a specific analysis engine that further prevents statistical inference attacks on analysis results. ESA extends existing best-practice methods for sensitive-data analytics, by using cryptography and statistical techniques to make explicit how data is elided and reduced in precision, how only common-enough, anonymous data is analyzed, and how this is done for only specific, permitted purposes. As a result, ESA remains compatible with the established workflows of traditional database analysis. Strong privacy guarantees, including differential privacy, can be established at each processing step to defend against malice or compromise at one or more of those steps. Prochlo develops new techniques to harden those steps, including the Stash Shuffle, a novel scalable and efficient oblivious-shuffling algorithm based on Intel's SGX, and new applications of cryptographic secret sharing and blinding. We describe ESA and Prochlo, as well as experiments that validate their ability to balance utility and privacy. Intel Software Guard Extensions (SGX) is a hardware-based Trusted Execution Environment (TEE) that is widely seen as a promising solution to traditional security threats. While SGX promises strong protection to bugfree software, decades of experience show that we have to expect vulnerabilities in any non-trivial application. In a traditional environment, such vulnerabilities often allow attackers to take complete control of vulnerable systems. Efforts to evaluate the security of SGX have focused on side-channels. So far, neither a practical attack against a vulnerability in enclave code nor a proof-of-concept attack scenario has been demonstrated. Thus, a fundamental question remains: What are the consequences and dangers of having a memory corruption vulnerability in enclave code? To answer this question, we comprehensively analyze exploitation techniques against vulnerabilities inside enclaves. We demonstrate a practical exploitation technique, called Dark-ROP, which can completely disarm the security guarantees of SGX. Dark-ROP exploits a memory corruption vulnerability in the enclave software through return-oriented programming (ROP). However Dark-ROP differs significantly from traditional ROP attacks because the target code runs under solid hardware protection. We overcome the problem of exploiting SGX-specific properties and obstacles by formulating a novel ROP attack scheme against SGX under practical assumptions. Specifically, we build several oracles that inform the attacker about the status of enclave execution. This enables him to launch the ROP attack while both code and data are hidden. In addition, we exfiltrate the enclave's code and data into a shadow application to fully control the execution environment. This shadow application emulates the enclave under the complete control of the attacker, using the enclave (through ROP calls) only to perform SGX operations such as reading the enclave's SGX crypto keys. Shielded execution based on Intel SGX provides strong security guarantees for legacy applications running on untrusted platforms. However, memory safety attacks such as Heartbleed can render the confidentiality and integrity properties of shielded execution completely ineffective. To prevent these attacks, the state-of-the-art memory-safety approaches can be used in the context of shielded execution. In this work, we first showcase that two prominent software- and hardware-based defenses, AddressSanitizer and Intel MPX respectively, are impractical for shielded execution due to high performance and memory overheads. This motivated our design of SGXBounds---an efficient memory-safety approach for shielded execution exploiting the architectural features of Intel SGX. Our design is based on a simple combination of tagged pointers and compact memory layout. We implemented SGXBounds based on the LLVM compiler framework targeting unmodified multithreaded applications. Our evaluation using Phoenix, PARSEC, and RIPE benchmark suites shows that SGXBounds has performance and memory overheads of 17 and 0.1 respectively, while providing security guarantees similar to AddressSanitizer and Intel MPX. We have obtained similar results with SPEC CPU2006 and four real-world case studies: SQLite, Memcached, Apache, and Nginx. ABSTRACTSmart grid (SG) allows for two-way communication between the utility and its consumers and hence they are considered as an inevitable future of the traditional grid. Since consumers are the key component of SGs, providing security and privacy to their personal data is a critical problem. In this paper, a security protocol, namely TPS3, is based on Temporal Perturbation and Shamir’s Secret Sharing (SSS) schemes that are proposed to ensure the privacy of SG consumer’s data. Temporal perturbation is employed to provide temporal privacy, while the SSS scheme is used to ensure data confidentiality. Temporal perturbation adds random delays to the data collected by smart meters, whereas the SSS scheme fragments these data before transmitting them to the data collection server. Joint employment of both schemes makes it hard for attackers to obtain consumer data collected in the SG. The proposed protocol TPS3 is evaluated in terms of privacy, reliability, and communication cost using two different SG topol...