ILP-based Local Search for Graph Partitioning

Computing high-quality graph partitions is a challenging problem with numerous applications. In this paper, we present a novel meta-heuristic for the balanced graph partitioning problem. Our approach is based on integer linear programs that solve the partitioning problem to optimality. However, since those programs typically do not scale to large inputs, we adapt them to heuristically improve a given partition. We do so by defining a much smaller model that allows us to use symmetry breaking and other techniques that make the approach scalable. For example, in Walshaw\u27s well-known benchmark tables we are able to improve roughly half of all entries when the number of blocks is high

Single-Server Private Information Retrieval with Sublinear Amortized Time

We construct new private-information-retrieval protocols in the single-server setting. Our schemes allow a client to privately fetch a sequence of database records from a server, while the server answers each query in average time sublinear in the database size. Specifically, we introduce the first single-server private-information-retrieval schemes that have sublinear amortized server time, require sublinear additional storage, and allow the client to make her queries adaptively. Our protocols rely only on standard cryptographic assumptions (decision Diffie-Hellman, quadratic residuosity, learning with errors, etc.). They work by having the client first fetch a small hint about the database contents from the server. Generating this hint requires server time linear in the database size. Thereafter, the client can use the hint to make a bounded number of adaptive queries to the server, which the server answers in sub-linear time--yielding sublinear amortized cost. Finally, we give lower bounds proving that our most efficient scheme is optimal with respect to the trade-off it achieves between server online time and client storage

Private Web Search with Tiptoe

Tiptoe is a private web search engine that allows clients to search over hundreds of millions of documents, while revealing no information about their search query to the search engine’s servers. Tiptoe’s privacy guarantee is based on cryptography alone; it does not require hardware enclaves or non-colluding servers. Tiptoe uses semantic embeddings to reduce the problem of private full-text search to private nearest-neighbor search. Then, Tiptoe implements private nearest-neighbor search with a new, high-throughput protocol based on linearly homomorphic encryption. Running on a 45-server cluster, Tiptoe can privately search over 360 million web pages with 145 core-seconds of server compute, 56.9 MiB of client-server communication (74% of which occurs before the client enters its search query), and 2.7 seconds of end-to-end latency. Tiptoe’s search works best on conceptual queries (“knee pain”) and less well on exact string matches (“123 Main Street, New York”). On the MS MARCO search-quality benchmark, Tiptoe ranks the best-matching result in position 7.7 on average. This is worse than a state-of-the-art, non-private neural search algorithm (average rank: 2.3), but is close to the classical tf-idf algorithm (average rank: 6.7). Finally, Tiptoe is extensible: it also supports private text-to-image search and, with minor modifications, it can search over audio, code, and more

One Server for the Price of Two: Simple and Fast Single-Server Private Information Retrieval

We present SimplePIR, the fastest single-server private information retrieval scheme known to date. SimplePIR’s security holds under the learning-with-errors assumption. To answer a client’s query, the SimplePIR server performs fewer than one 32-bit multiplication and one 32-bit addition per database byte. SimplePIR achieves 10 GB/s/core server throughput, which approaches the memory bandwidth of the machine and the performance of the fastest two-server private-information-retrieval schemes (which require non-colluding servers). SimplePIR has relatively large communication costs: to make queries to a 1 GB database, the client must download a 121 MB hint about the database contents; thereafter, the client may make an unbounded number of queries, each requiring 242 KB of communication. We present a second single-server scheme, DoublePIR, that shrinks the hint to 16 MB at the cost of slightly higher per-query communication (345 KB) and slightly lower throughput (7.4 GB/s/core). Finally, we apply our new private-information-retrieval schemes, together with a novel data structure for approximate set membership, to the task of private auditing in Certificate Transparency. We achieve a strictly stronger notion of privacy than Google Chrome’s current approach with modest communication overheads: 16 MB of download per month, along with 150 bytes per TLS connection

Single-Server Private Information Retrieval with Sublinear Amortized Time

We construct new private-information-retrieval protocols in the singleserver setting. Our schemes allow a client to privately fetch a sequence of database records from a server, while the server answers each query in average time sublinear in the database size. Specifically, we introduce the first single-server private-information-retrieval schemes that have sublinear amortized server time, require sublinear additional storage, and allow the client to make her queries adaptively. Our protocols rely only on standard cryptographic assumptions (decision Diffie-Hellman, quadratic residuosity, learning with errors, etc.). They work by having the client first fetch a small “hint” about the database contents from the server. Generating this hint requires server time linear in the database size. Thereafter, the client can use the hint to make a bounded number of adaptive queries to the server, which the server answers in sublinear time—yielding sublinear amortized cost. Finally, we give lower bounds proving that our most efficient scheme is optimal with respect to the trade-off it achieves between server online time and client storage.S.M