GraphSE2^2: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE$^2$, an encrypted graph database for online social network services to address massive data breaches. GraphSE$^2$ preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE$^2$ provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE$^2$ with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE$^2$ is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE$^2$: An Encrypted Graph Database for Privacy-Preserving Social Search". It includes the security proof of the proposed scheme. If you want to cite our work, please cite the conference version of i

Dynamic block encryption with self-authenticating key exchange

One of the greatest challenges facing cryptographers is the mechanism used for key exchange. When secret data is transmitted, the chances are that there may be an attacker who will try to intercept and decrypt the message. Having done so, he/she might just gain advantage over the information obtained, or attempt to tamper with the message, and thus, misguiding the recipient. Both cases are equally fatal and may cause great harm as a consequence. In cryptography, there are two commonly used methods of exchanging secret keys between parties. In the first method, symmetric cryptography, the key is sent in advance, over some secure channel, which only the intended recipient can read. The second method of key sharing is by using a public key exchange method, where each party has a private and public key, a public key is shared and a private key is kept locally. In both cases, keys are exchanged between two parties. In this thesis, we propose a method whereby the risk of exchanging keys is minimised. The key is embedded in the encrypted text using a process that we call `chirp coding', and recovered by the recipient using a process that is based on correlation. The `chirp coding parameters' are exchanged between users by employing a USB flash memory retained by each user. If the keys are compromised they are still not usable because an attacker can only have access to part of the key. Alternatively, the software can be configured to operate in a one time parameter mode, in this mode, the parameters are agreed upon in advance. There is no parameter exchange during file transmission, except, of course, the key embedded in ciphertext. The thesis also introduces a method of encryption which utilises dynamic blocks, where the block size is different for each block. Prime numbers are used to drive two random number generators: a Linear Congruential Generator (LCG) which takes in the seed and initialises the system and a Blum-Blum Shum (BBS) generator which is used to generate random streams to encrypt messages, images or video clips for example. In each case, the key created is text dependent and therefore will change as each message is sent. The scheme presented in this research is composed of five basic modules. The first module is the key generation module, where the key to be generated is message dependent. The second module, encryption module, performs data encryption. The third module, key exchange module, embeds the key into the encrypted text. Once this is done, the message is transmitted and the recipient uses the key extraction module to retrieve the key and finally the decryption module is executed to decrypt the message and authenticate it. In addition, the message may be compressed before encryption and decompressed by the recipient after decryption using standard compression tools

Approximate Data Analytics Systems

Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. Unfortunately, the advancements in approximate computing are primarily geared towards batch analytics and cannot provide low-latency guarantees in the context of stream processing, where new data continuously arrives as an unbounded stream. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale stream data to achieve low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: • StreamApprox—a data stream analytics system for approximate computing. This system supports approximate computing for low-latency stream analytics in a transparent way and has an ability to adapt to rapid fluctuations of input data streams. In this system, we designed an online adaptive stratified reservoir sampling algorithm to produce approximate output with bounded error. • IncApprox—a data analytics system for incremental approximate computing. This system adopts approximate and incremental computing in stream processing to achieve high-throughput and low-latency with efficient resource utilization. In this system, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. • PrivApprox—a data stream analytics system for privacy-preserving and approximate computing. This system supports high utility and low-latency data analytics and preserves user’s privacy at the same time. The system is based on the combination of privacy-preserving data analytics and approximate computing. • ApproxJoin—an approximate distributed joins system. This system improves the performance of joins — critical but expensive operations in big data systems. In this system, we employed a sketching technique (Bloom filter) to avoid shuffling non-joinable data items through the network as well as proposed a novel sampling mechanism that executes during the join to obtain an unbiased representative sample of the join output. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance speedup compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications

Remote dynamic partial reconfiguration: A threat to Internet-of-Things and embedded security applications

The advent of the Internet of Things has motivated the use of Field Programmable Gate Array (FPGA) devices with Dynamic Partial Reconfiguration (DPR) capabilities for dynamic non-invasive modifications to circuits implemented on the FPGA. In particular, the ability to perform DPR over the network is essential in the context of a growing number of Internet of Things (IoT)-based and embedded security applications. However, the use of remote DPR brings with it a number of security threats that could lead to potentially catastrophic consequences in practical scenarios. In this paper, we demonstrate four examples where the remote DPR capability of the FPGA may be exploited by an adversary to launch Hardware Trojan Horse (HTH) attacks on commonly used security applications. We substantiate the threat by demonstrating remotely-launched attacks on Xilinx FPGA-based hardware implementations of a cryptographic algorithm, a true random number generator, and two processor-based security applications - namely, a software implementation of a cryptographic algorithm and a cash dispensing scheme. The attacks are launched by on-the-fly transfer of malicious FPGA configuration bitstreams over an Ethernet connection to perform DPR and leak sensitive information. Finally, we comment on plausible countermeasures to prevent such attack

Functional encryption based approaches for practical privacy-preserving machine learning

Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical. In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient

Verified Implementations for Secure and Verifiable Computation

Formal verification of the security of software systems is gradually moving from the traditional focus on idealized models, to the more ambitious goal of producing verified implementations. This trend is also present in recent work targeting the verification of cryptographic software, but the reach of existing tools has so far been limited to cryptographic primitives, such as RSA-OAEP encryption, or standalone protocols, such as SSH. This paper presents a scalable approach to formally verifying implementations of higher-level cryptographic systems, directly in the computational model. We consider circuit-based cloud-oriented cryptographic protocols for secure and verifiable computation over encrypted data. Our examples share as central component Yao\u27s celebrated transformation of a boolean circuit into an equivalent garbled form that can be evaluated securely in an untrusted environment. We leverage the foundations of garbled circuits set forth by Bellare, Hoang, and Rogaway (CCS 2012, ASIACRYPT 2012) to build verified implementations of garbling schemes, a verified implementation of Yao\u27s secure function evaluation protocol, and a verified (albeit partial) implementation of the verifiable computation protocol by Gennaro, Gentry, and Parno (CRYPTO 2010). The implementations are formally verified using EasyCrypt, a tool-assisted framework for building high-confidence cryptographic proofs, and critically rely on two novel features: a module and theory system that supports compositional reasoning, and a code extraction mechanism for generating implementations from formalizations

Remote dynamic partial reconfiguration: A threat to Internet-of-Things and embedded security applications

The advent of the Internet of Things has motivated the use of Field Programmable Gate Array (FPGA) devices with Dynamic Partial Reconfiguration (DPR) capabilities for dynamic non-invasive modifications to circuits implemented on the FPGA. In particular, the ability to perform DPR over the network is essential in the context of a growing number of Internet of Things (IoT)-based and embedded security applications. However, the use of remote DPR brings with it a number of security threats that could lead to potentially catastrophic consequences in practical scenarios. In this paper, we demonstrate four examples where the remote DPR capability of the FPGA may be exploited by an adversary to launch Hardware Trojan Horse (HTH) attacks on commonly used security applications. We substantiate the threat by demonstrating remotely-launched attacks on Xilinx FPGA-based hardware implementations of a cryptographic algorithm, a true random number generator, and two processor-based security applications - namely, a software implementation of a cryptographic algorithm and a cash dispensing scheme. The attacks are launched by on-the-fly transfer of malicious FPGA configuration bitstreams over an Ethernet connection to perform DPR and leak sensitive information. Finally, we comment on plausible countermeasures to prevent such attack

FPGA-based true random number generation using circuit metastability with adaptive feedback control

13th International Workshop, Nara, Japan, September 28 – October 1, 2011. ProceedingsThe paper presents a novel and efficient method to generate true random numbers on FPGAs by inducing metastability in bi-stable circuit elements, e.g. flip-flops. Metastability is achieved by using precise programmable delay lines (PDL) that accurately equalize the signal arrival times to flip-flops. The PDLs are capable of adjusting signal propagation delays with resolutions higher than fractions of a pico second. In addition, a real time monitoring system is utilized to assure a high degree of randomness in the generated output bits, resilience against fluctuations in environmental conditions, as well as robustness against active adversarial attacks. The monitoring system employs a feedback loop that actively monitors the probability of output bits; as soon as any bias is observed in probabilities, it adjusts the delay through PDLs to return to the metastable operation region. Implementation on Xilinx Virtex 5 FPGAs and results of NIST randomness tests show the effectiveness of our approach